Autonomic aging - A dataset to quantify changes of cardiovascular autonomic function during healthy aging

The impact of long COVID on heart rate variability: a cross-sectional study

BACKGROUND

Long-term COVID-19 (LC), which may affect the autonomic nervous system (ANS), is the term for the symptoms that some patients had for an additional month after contracting the virus. Therefore, during the LC phase, ANS status was evaluated in patients with mild-to-moderate COVID-19 using heart rate variability (HRV), a measurement of ANS function.

METHODS

A cross-sectional research with 173 participants - both positive and negative for COVID-19 - was conducted. Based on self-reports, patients with COVID-19 were classified as to whether they had LC or not. A 5-minute ECG recorder and data detection and response report were used to measure the ANS.

RESULTS

There were notable age differences across the groups (p = 0.034). Patients with LC under 25 years of age had a lower HRV categorized as a very-low-frequency (VLF) domain (p = 0.012). Compared to the group without LC, a higher number of people in the LC group had aberrant autonomic neuroactivity (p = 0.048).

CONCLUSION

Mild-to-moderate patients with COVID-19 in young to middle age may develop autonomic dysfunction one month after infection.

One-week test-retest stability of heart rate variability during rest and deep breathing

Objective. Heart rate variability (HRV) is an important indicator of cardiac autonomic function. Given its clinical significance, reliable HRV assessment is crucial. Here, we assessed test-retest stability, as a key aspect of reliability, quantifying the consistency of a measure when repeated under the same conditions.Approach. This observational study includes healthy individuals. A 20 min electrocardiogram was recorded at rest in a supine position and during deep breathing in two lab sessions within one week, at the same time of day. HRV indices from time domain, frequency domain, nonlinear dynamics, and information-theoretic complexity were assessed using a validated toolbox. Additionally, heart rate variations per respiratory cycle were evaluated during deep breathing. Lifestyle factors such as perceived stress, mood, physical activity, sleep quality were assessed prior to both sessions. Intra-class correlation (ICC) and coefficients of variation (CVs) were used to assess the concordance between the two measurements and the relative deviation, respectively.Main results. From 62 screened individuals, 51 participants were recruited from the local community. One participant opted out for personal reasons, and another with frequent premature beats was excluded, leaving a final sample of 49 individuals. Most self-rated psychological and lifestyle indicators showed substantial agreement, though participants reported less stress and better mood in the second session. At rest, ICC of HRV ranged from 0.50 to 0.83, with CV from 5% to 41%. Spectral HRV measures were less reliable than time domain parameters. Nonlinear and time domain features had substantial to nearly perfect agreement. Complexity measures had low CVs but limited test-retest correlation. The stability indices of HRV during deep breathing were not significantly different from those during rest. Test-retest differences in root mean square of the successive beat-to-beat interval difference were not sufficiently explained by lifestyle factors.Significance.Test-retest stability of HRV depends considerably on chosen measures. Our data suggest that HRV can be assessed reliably using time-domain indices at rest.

One-week test-retest recordings of resting cardiorespiratory data for reliability analysis

Heart rate variability (HRV) is a key indicator of cardiac autonomic function, making reliable assessment crucial. To examine the test-retest stability of resting HRV in healthy individuals, fifty participants attended two lab sessions within a week, at the same time of day. After a 5-minute acclimatization period, electrocardiogram and respiration were recorded at rest. For validation, average heart rate and RMSSD were assessed over 15 minutes using a validated open-source toolbox. Test-retest agreement was evaluated using intra-class correlation (ICC), and coefficients of variation (CV). Mean heart rate showed high stability (ICC = 0.81, CV = 6%), while RMSSD had lower concordance (ICC = 0.75) and greater variation (CV = 30%). These findings indicate good test-retest agreement for standard HRV features. However, a wide range of methodologies exists for assessing various properties of heart rate dynamics. This database is intended to support other researchers in testing additional HRV metrics to evaluate their reliability in healthy individuals.

Synthetic ECG signals generation: A scoping review

The scientific community has recently shown increasing interest in generating synthetic ECG data. In particular, synthetic ECG signals can be beneficial for understanding cardiac electrical activity, developing large and heterogeneous unbiased datasets, and anonymizing data to favour knowledge sharing and open science. In the present scoping review, various methodologies to generate synthetic ECG data have been thoroughly analysed, highlighting their limitations and possibilities. A total of 79 studies have been included and classified, depending on the methodology employed, the number of leads, the number of heartbeats, and the purpose of data synthesis. Three main categories have been identified: mathematical modelling, computer vision inherited methods, and deep generative models. This thorough analysis can assist in the choice of the most suitable technique for a specific application. The biggest challenge is identifying standardized metrics that can comprehensively and quantitatively assess the fidelity and variability of generated synthetic ECG data.

Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool

Medical datasets are vital for advancing Artificial Intelligence (AI) in healthcare. Yet biases in these datasets on which deep-learning models are trained can compromise reliability. This study investigates biases stemming from dataset-creation practices. Drawing on existing guidelines, we first developed a BEAMRAD tool to assess the documentation of public Magnetic Resonance Imaging (MRI); Color Fundus Photography (CFP), and Electrocardiogram (ECG) datasets. In doing so, we provide an overview of the biases that may emerge due to inadequate dataset documentation. Second, we examine the current state of documentation for public medical images and signal data. Our research reveals that there is substantial variance in the documentation of image and signal datasets, even though guidelines have been developed in medical imaging. This indicates that dataset documentation is subject to individual discretionary decisions. Furthermore, we find that aspects such as hardware and data acquisition details are commonly documented, while information regarding data annotation practices, annotation error quantification, or data limitations are not consistently reported. This risks having considerable implications for the abilities of data users to detect potential sources of bias through these respective aspects and develop reliable and robust models that can be adapted for clinical practice.

White matter differences between younger and older adults revealed by fixel-based analysis

The process of healthy aging involves complex alterations in neural structures, with white matter (WM) changes significantly impacting cognitive and motor functions. Conventional methods such as diffusion tensor imaging provide valuable insights, but their limitations in capturing complex WM geometry advocate for more advanced approaches. In this study involving 120 healthy volunteers, we investigated whole-brain WM differences between young and old individuals using a novel technique called fixel-based analysis (FBA). This approach revealed that older adults exhibited reduced FBA-derived metrics in several WM tracts, with frontal areas particularly affected. Surprisingly, age-related differences in FBA-derived measures showed no significant correlation with risk factors such as alcohol consumption, exercise frequency, or pulse pressure but predicted cognitive performance. These findings emphasize FBA's potential in characterizing complex WM changes and the link between cognitive abilities and WM alterations in healthy aging. Overall, this study advances our understanding of age-related neurodegeneration, highlighting the importance of comprehensive assessments that integrate advanced neuroimaging techniques, cognitive evaluation, and demographic factors to gain insights into healthy aging.

Are Wearable Photoplethysmogram-Based Heart Rate Variability Measures Equivalent to Electrocardiogram? A Simulation Study

BACKGROUND

Traditional electrocardiography (ECG)-derived heart rate variability (HRV) and photoplethysmography (PPG)-derived "HRV" (termed PRV) have been reported interchangeably. Any potential dissociation between HRV and PRV could be due to the variability in pulse arrival time (PAT; time between heartbeat and peripheral pulse).

OBJECTIVE

This study examined if PRV is equivalent to ECG-derived HRV and if PRV's innate error makes it a high-quality measurement separate from HRV.

METHODS

ECG data from 1084 subjects were obtained from the PhysioNet Autonomic Aging dataset, and individual PAT dispersions for both the wrist (n = 42) and finger (n = 49) were derived from Mol et al. (Exp Gerontol. 2020; 135: 110938). A Bayesian simulation was constructed whereby the individual arrival times of the PPG wave were calculated by placing a Gaussian prior on the individual QRS-wave timings of each ECG series. The standard deviation (σ) of the prior corresponds to the PAT dispersion from Mol et al. This was simulated 10,000 times for each PAT σ. The root mean square of successive differences (RMSSD) and standard deviation of N-N intervals (SDNN) were calculated for both HRV and PRV. The Region of Practical Equivalence bounds (ROPE) were set a priori at ± 0.2% of true HRV. The highest density interval (HDI) width, encompassing 95% of the posterior distribution, was calculated for each PAT σ.

RESULTS

The lowest PAT σ (2.0 SD) corresponded to 88.4% within ROPE for SDNN and 21.4% for RMSSD. As the σ of PAT increases, the equivalence of PRV and HRV decreases for both SDNN and RMSSD. The HDI interval width increases with increasing PAT σ, with the HDI width increasing at a higher rate for RMSSD than SDNN.

CONCLUSIONS

For individuals with greater PAT variability, PRV is not a surrogate for HRV. When considering PRV as a unique biometric measure, SDNN may have more favorable measurement properties than RMSSD, though both exhibit a non-uniform measurement error.

Axon guidance genes are regulated by TDP-43 and RGNEF through long-intron removal

Rho guanine nucleotide exchange factor (RGNEF) is a guanine nucleotide exchange factor (GEF) mainly involved in regulating the activity of Rho-family GTPases. It is a bi-functional protein, acting both as a guanine exchange factor and as an RNA-binding protein. RGNEF is known to act as a destabilizing factor of neurofilament light chain RNA (NEFL) and it could potentially contribute to their sequestration in nuclear cytoplasmic inclusions. Most importantly, RGNEF inclusions in the spinal motor neurons of ALS patients have been shown to co-localize with inclusions of TDP-43, the major well-known RNA-binding protein aggregating in the brain and spinal cord of human patients. Therefore, it can be hypothesized that loss-of-function of both proteins following aggregation may contribute to motor neuron death/survival in ALS patients. To further characterize their relationship, we have compared the transcriptomic profiles of neuronal cells depleted of TDP-43 and RGNEF and show that these two factors predominantly act in an antagonistic manner when regulating the expression of axon guidance genes. From a mechanistic point of view, our experiments show that the effect of these genes on the processivity of long introns can explain their mode of action. Taken together, our results show that loss-of-function of factors co-aggregating with TDP-43 can potentially affect the expression of commonly regulated neuronal genes in a very significant manner, potentially acting as disease modifiers. This finding further highlights that neurodegenerative processes at the RNA level are the result of combinatorial interactions between different RNA-binding factors that can be co-aggregated in neuronal cells. A deeper understanding of these complex scenarios may lead to a better understanding of pathogenic mechanisms occurring in patients, where more than one specific protein may be aggregating in their neurons.

Artificial-intelligence-based risk prediction and mechanism discovery for atrial fibrillation using heart beat-to-beat intervals

BACKGROUND

Early diagnosis of atrial fibrillation (AF) is important for preventing stroke and other complications. Predicting AF risk in advance can improve early diagnostic efficiency. Deep learning has been used for disease risk prediction; however, it lacks adherence to evidence-based medicine standards. Identifying the underlying mechanisms behind disease risk prediction is important and required.

METHODS

We developed an explainable deep learning model called HBBI-AI to predict AF risk using only heart beat-to-beat intervals (HBBIs) during sinus rhythm. We proposed a possible AF mechanism based on the model's explainability and verified this conjecture using confirmed AF risk factors while also examining new AF risk factors. Finally, we investigated the changes in clinicians' ability to predict AF risk using only HBBIs before and after learning the model's explainability.

FINDINGS

HBBI-AI consistently performed well across large in-house and external public datasets. HBBIs with large changes or extreme stability were critical predictors for increased AF risk, and the underlying cause was autonomic imbalance. We verified various AF risk factors and discovered that autonomic imbalance was associated with all these factors. Finally, cardiologists effectively understood and learned from these findings to improve their abilities in AF risk prediction.

CONCLUSIONS

HBBI-AI effectively predicted AF risk using only HBBI information through evaluating autonomic imbalance. Autonomic imbalance may play an important role in many risk factors of AF rather than in a limited number of risk factors.

FUNDING

This study was supported in part by the National Key R&D Program and the National Natural Science Foundation of China.

Using explainable AI to investigate electrocardiogram changes during healthy aging-From expert features to raw signals

Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.

An extensive quantitative analysis of the effects of errors in beat-to-beat intervals on all commonly used HRV parameters

Heart rate variability (HRV) analysis is often used to estimate human health and fitness status. More specifically, a range of parameters that express the variability in beat-to-beat intervals are calculated from electrocardiogram beat detections. Since beat detection may yield erroneous interval data, these errors travel through the processing chain and may result in misleading parameter values that can lead to incorrect conclusions. In this study, we utilized Monte Carlo simulation on real data, Kolmogorov-Smirnov tests and Bland-Altman analysis to carry out extensive analysis of the noise sensitivity of different HRV parameters. The used noise models consider Gaussian and student-t distributed noise. As a result we observed that commonly used HRV parameters (e.g. pNN50 and LF/HF ratio) are especially sensitive to noise and that all parameters show biases to some extent. We conclude that researchers should be careful when reporting different HRV parameters, consider the distributions in addition to mean values, and consider reference data if applicable. The analysis of HRV parameter sensitivity to noise and resulting biases presented in this work generalizes over a wide population and can serve as a reference and thus provide a basis for the decision about which HRV parameters to choose under similar conditions.

Sex differences in the age-related decrease of spontaneous baroreflex function in healthy individuals

The baroreflex is a powerful physiological mechanism for rapidly adjusting heart rate in response to changes in blood pressure. Spontaneous baroreflex sensitivity (BRS) has been shown to decrease with age. However, studies of sex differences in these age-related changes are rare. Here we investigated several markers of spontaneous baroreflex function in a large sample of healthy individuals. Cardiovascular signals were recorded in the supine position under carefully controlled resting conditions. After quality control, n = 980 subjects were divided into five age groups [age < 30 yr (n = 612), 30-39 yr (n = 140), 40-49 yr (n = 95), 50-59 yr (n = 61), and >60 yr (n = 72)]. Spontaneous baroreflex function was assessed in the time domain (bradycardic and tachycardic slope) and in the frequency domain in the low- and high-frequency band (LF-α, HF-α) applying the transfer function. General linear models showed a significant effect of factor age (P < 0.001) and an age × sex interaction effect (P < 0.05) on each indicator of the baroreflex function. Simple main effects showed a significantly higher BRS as indicated by tachycardic slope, LF-α and HF-α in middle-aged women compared with men (30-39 yr) and higher LF-α, bradycardic and tachycardic slope in men compared with women of the oldest age group (>60 yr). Changes in BRS over the lifespan suggest that baroreflex function declines more slowly but earlier in life in men than in women. Our findings could be linked to age-related changes in major sex hormone levels, suggesting significant implications for diverse cardiovascular outcomes and the implementation of targeted preventive strategies.NEW & NOTEWORTHY In this study, we demonstrate that the age-related decrease of spontaneous baroreflex sensitivity is different in men and women by analyzing resting state cardiovascular data of a large sample of healthy individuals.

Revisiting nonlinearity of heart rate variability in healthy aging

Aging is commonly regarded as a physiological process in which the dynamic complexity of physiological time series and organ systems is gradually lost. This notion is derived from the identification of a decline of nonlinear measures with the advance of aging. However, additional research on cardiovascular control studied through heart rate variability (HRV), i.e., the instantaneous changes in heart rate, shows that despite the constriction of its statistical distribution, the nonlinear organization remains present in advanced age. Here, we used surrogate data testing to investigate the presence of nonlinear information in HRV time series from a publicly available database of 1121 healthy human subjects from 18 to 92 years old. We also studied the influence of basic clinical features, such as sex, body mass index (BMI), and mean heart rate (HR), on such nonlinear information. We found that the percentage of nonlinear time series after 30 years of age diminishes significantly (p < 0.01). Furthermore, larger BMI and HR are associated with the presence of more linear information in HRV, while the female sex is associated with the manifestation of nonlinear information. This work provides a common background for the contextualized interpretation of nonlinear testing and shows that the nonlinear content of HRV time series diminishes through aging.

Multimodal nocturnal seizure detection in children with epilepsy: A prospective, multicenter, long-term, in-home trial

OBJECTIVE

There is a pressing need for reliable automated seizure detection in epilepsy care. Performance evidence on ambulatory non-electroencephalography-based seizure detection devices is low, and evidence on their effect on caregiver's stress, sleep, and quality of life (QoL) is still lacking. We aimed to determine the performance of NightWatch, a wearable nocturnal seizure detection device, in children with epilepsy in the family home setting and to assess its impact on caregiver burden.

METHODS

We conducted a phase 4, multicenter, prospective, video-controlled, in-home NightWatch implementation study (NCT03909984). We included children aged 4-16 years, with ≥1 weekly nocturnal major motor seizure, living at home. We compared a 2-month baseline period with a 2-month NightWatch intervention. The primary outcome was the detection performance of NightWatch for major motor seizures (focal to bilateral or generalized tonic-clonic [TC] seizures, focal to bilateral or generalized tonic seizures lasting >30 s, hyperkinetic seizures, and a remainder category of focal to bilateral or generalized clonic seizures and "TC-like" seizures). Secondary outcomes included caregivers' stress (Caregiver Strain Index [CSI]), sleep (Pittsburgh Quality of Sleep Index), and QoL (EuroQol five-dimension five-level scale).

RESULTS

We included 53 children (55% male, mean age = 9.7 ± 3.6 years, 68% learning disability) and analyzed 2310 nights (28 173 h), including 552 major motor seizures. Nineteen participants did not experience any episode of interest during the trial. The median detection sensitivity per participant was 100% (range = 46%-100%), and the median individual false alarm rate was .04 per hour (range = 0-.53). Caregiver's stress decreased significantly (mean total CSI score = 8.0 vs. 7.1, p = .032), whereas caregiver's sleep and QoL did not change significantly during the trial.

SIGNIFICANCE

The NightWatch system demonstrated high sensitivity for detecting nocturnal major motor seizures in children in a family home setting and reduced caregiver stress.

Evaluating heart rate variability with 10 second multichannel electrocardiograms in a large population-based sample

Introduction

Heart rate variability (HRV), defined as the variability of consecutive heart beats, is an important biomarker for dysregulations of the autonomic nervous system (ANS) and is associated with the development, course, and outcome of a variety of mental and physical health problems. While guidelines recommend using 5 min electrocardiograms (ECG), recent studies showed that 10 s might be sufficient for deriving vagal-mediated HRV. However, the validity and applicability of this approach for risk prediction in epidemiological studies is currently unclear to be used.

Methods

This study evaluates vagal-mediated HRV with ultra-short HRV (usHRV) based on 10 s multichannel ECG recordings of N = 4,245 and N = 2,392 participants of the Study of Health in Pomerania (SHIP) from two waves of the SHIP-TREND cohort, additionally divided into a healthy and health-impaired subgroup. Association of usHRV with HRV derived from long-term ECG recordings (polysomnography: 5 min before falling asleep [N = 1,041]; orthostatic testing: 5 min of rest before probing an orthostatic reaction [N = 1,676]) and their validity with respect to demographic variables and depressive symptoms were investigated.

Results

High correlations (r = .52-.75) were revealed between usHRV and HRV. While controlling for covariates, usHRV was the strongest predictor for HRV. Furthermore, the associations of usHRV and HRV with age, sex, obesity, and depressive symptoms were similar.

Conclusion

This study provides evidence that usHRV derived from 10 s ECG might function as a proxy of vagal-mediated HRV with similar characteristics. This allows the investigation of ANS dysregulation with ECGs that are routinely performed in epidemiological studies to identify protective and risk factors for various mental and physical health problems.

Autonomic Function Recovery and Physical Activity Levels in Post-COVID-19 Young Adults after Immunization: An Observational Follow-Up Case-Control Study

Coronavirus disease 2019 (COVID-19) has detrimental multi-system consequences. Symptoms may appear during the acute phase of infection, but the literature on long-term recovery of young adults after mild to moderate infection is lacking. Heart rate variability (HRV) allows for the observation of autonomic nervous system (ANS) modulation post-SARS-CoV-2 infection. Since physical activity (PA) can help improve ANS modulation, investigating factors that can influence HRV outcomes after COVID-19 is essential to advancements in care and intervention strategies. Clinicians may use this research to aid in the development of non-medication interventions. At baseline, 18 control (CT) and 20 post-COVID-19 (PCOV) participants were observed where general anamnesis was performed, followed by HRV and PA assessment. Thus, 10 CT and 7 PCOV subjects returned for follow-up (FU) evaluation 6 weeks after complete immunization (two doses) and assessments were repeated. Over the follow-up period, a decrease in sympathetic (SNS) activity (mean heart rate: p = 0.0024, CI = -24.67--3.26; SNS index: p = 0.0068, CI = -2.50--0.32) and increase in parasympathetic (PNS) activity (mean RR: p = 0.0097, CI = 33.72-225.51; PNS index: p = 0.0091, CI = -0.20-1.47) were observed. At follow-up, HRV was not different between groups (p > 0.05). Additionally, no differences were observed in PA between moments and groups. This study provides evidence of ANS recovery after SARS-CoV-2 insult in young adults over a follow-up period, independent of changes in PA.

Using machine learning to estimate the calendar age based on autonomic cardiovascular function

Introduction

Aging is accompanied by physiological changes in cardiovascular regulation that can be evaluated using a variety of metrics. In this study, we employ machine learning on autonomic cardiovascular indices in order to estimate participants' age.

Methods

We analyzed a database including resting state electrocardiogram and continuous blood pressure recordings of healthy volunteers. A total of 884 data sets met the inclusion criteria. Data of 72 other participants with an BMI indicating obesity (>30 kg/m²) were withheld as an evaluation sample. For all participants, 29 different cardiovascular indices were calculated including heart rate variability, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics. Based on cardiovascular indices, sex and device, four different approaches were applied in order to estimate the calendar age of healthy subjects, i.e., relevance vector regression (RVR), Gaussian process regression (GPR), support vector regression (SVR), and linear regression (LR). To estimate age in the obese group, we drew normal-weight controls from the large sample to build a training set and a validation set that had an age distribution similar to the obesity test sample.

Results

In a five-fold cross validation scheme, we found the GPR model to be suited best to estimate calendar age, with a correlation of r=0.81 and a mean absolute error of MAE=5.6 years. In men, the error (MAE=5.4 years) seemed to be lower than that in women (MAE=6.0 years). In comparison to normal-weight subjects, GPR and SVR significantly overestimated the age of obese participants compared with controls. The highest age gap indicated advanced cardiovascular aging by 5.7 years in obese participants.

Discussion

In conclusion, machine learning can be used to estimate age on cardiovascular function in a healthy population when considering previous models of biological aging. The estimated age might serve as a comprehensive and readily interpretable marker of cardiovascular function. Whether it is a useful risk predictor should be investigated in future studies.

Association between Polypharmacy and Cardiovascular Autonomic Function among Elderly Patients in an Urban Municipality Area of Kolkata, India: A Record-Based Cross-Sectional Study

We assessed the association between polypharmacy and cardiovascular autonomic function among community-dwelling elderly patients having chronic diseases. Three hundred and twenty-one patients from an urban municipality area of Kolkata, India were studied in August 2022. The anticholinergic burden and cardiac autonomic function (Valsalva ratio, orthostatic hypotension, change in diastolic blood pressure after an isometric exercise, and heart rate variability during expiration and inspiration) were evaluated. Binary logistic regression analysis was performed to find out the association of polypharmacy and total anticholinergic burden with cardiac autonomic neuropathy. A total of 305 patients (age, 68.9 ± 3.4; 65.9% male) were included. Of these patients, 81 (26.6%) were on polypharmacy. Out of these 81 patients, 42 patients were on ninety-eight potential inappropriate medications. The anticholinergic burden and the proportion of patients with cardiac autonomic neuropathy were significantly higher among patients who were on polypharmacy than those who were not (8.1 ± 2.3 vs. 2.3 ± 0.9; p = 0.03 and 56.8% vs. 44.6%; p = 0.01). The presence of polypharmacy and a total anticholinergic burden of > 3 was significantly associated with cardiac autonomic neuropathy (aOR, 2.66; 95% CI, 0.91−3.98 and aOR, 2.51; 95% CI, 0.99−3.52, respectively). Thus, polypharmacy was significantly associated with cardiac autonomic neuropathy among community-dwelling elderly patients.

Going beyond the DSM in predicting, diagnosing, and treating autism spectrum disorder with covarying alexithymia and OCD: A structural equation model and process-based predictive coding account

Background

There is much overlap among the symptomology of autistic spectrum disorders (ASDs), obsessive compulsive disorders (OCDs), and alexithymia, which all typically involve impaired social interactions, repetitive impulsive behaviors, problems with communication, and mental health.

Aim

This study aimed to identify direct and indirect associations among alexithymia, OCD, cardiac interoception, psychological inflexibility, and self-as-context, with the DV ASD and depression, while controlling for vagal related aging.

Methodology

The data involved electrocardiogram (ECG) heart rate variability (HRV) and questionnaire data. In total, 1,089 participant's data of ECG recordings of healthy resting state HRV were recorded and grouped into age categories. In addition to this, another 224 participants completed an online survey that included the following questionnaires: Yale-Brown Obsessive Compulsive Scale (Y-BOCS); Toronto Alexithymia Scale 20 (TAS-20); Acceptance and Action Questionnaire (AAQII); Depression, Anxiety, and Stress Scale 21 (DAS21); Multi-dimensional Assessment of Interoceptive Awareness Scale (MAIA); and the Self-as-Context Scale (SAC).

Results

Heart rate variability was shown to decrease with age when controlling for BMI and gender. In the two SEMs produced, it was found that OCD and alexithymia were causally associated with autism and depression indirectly through psychological inflexibility, SAC, and ISen interoception.

Conclusion

The results are discussed in relation to the limitations of the DSM with its categorical focus of protocols for syndromes and provide support for more flexible ideographic approaches in diagnosing and treating mental health and autism within the Extended Evolutionary Meta-Model (EEMM). Graph theory approaches are discussed in their capacity to depict the processes of change potentially even at the level of the relational frame.

Autonomic aging - A dataset to quantify changes of cardiovascular autonomic function during healthy aging

Autonomic regulation of blood pressure and cardiac rhythm progressively declines with increasing age. Impaired cardiovascular control promotes a variety of age-related cardio-vascular conditions. This study aims to provide a database of high-resolution biological signals to describe the effect of healthy aging on cardiovascular regulation. Electrocardiogram and continuous non-invasive blood pressure signals were recorded simultaneously at rest in 1,121 healthy volunteers. With this database, we provide raw signals as well as basic demographic information such as gender and body mass index. To demonstrate validity of the acquired data, we present the well-known decline of heart rate variability with increasing age in this database.

Measurement(s)	Cardiovascular autonomic function
Technology Type(s)	Physiological signals
Sample Characteristic - Organism	Homo sapiens