Impaired circadian heart rate variability in Parkinson's disease: a time-domain analysis in ambulatory setting

Unraveling Autonomic Dysfunction in GBA-Related Parkinson's Disease

Background

Patients with Parkinson's disease (PD) and GBA gene mutations (GBA-PD) develop nonmotor complications more frequently than noncarriers. However, an objective characterization of both cardiovascular and sudomotor autonomic dysfunction using extensive clinical and instrumental measures has never been provided so far. Survival is reduced in GBA-PD regardless of age and dementia, suggesting that other hitherto unrecognized factors are involved.

Objectives

To provide instrumental measures of pattern and severity of autonomic dysfunction in GBA-PD and explore their correlation with other non-motor symptoms and implications for clinical practice.

Methods

In this cross-sectional study, 21 GBA-PD and 24 matched PD noncarriers underwent extensive assessment of motor and non-motor features, including neuropsychological testing. Cardiovascular autonomic function was explored through a comprehensive battery of indexes, including power spectral analysis of the R-R intervals and blood pressure short-term variability during resting state and active maneuvers. Dynamic Sweat Test was used to assess post-ganglionic sudomotor dysfunction.

Results

Despite minimal or absent clinical correlates, cardiovagal and sympathetic indexes, heart rate variability parameters and sudomotor postganglionic function were more severely impaired in GBA-PD than noncarriers (overcoming relatively preserved compensatory peripheral sympathetic function), suggesting more prominent cardiac sympatho-vagal demodulation, efferent baroreflex failure and peripheral sympathetic dysfunction in GBA-PD. Cardiovascular dysautonomia showed marginal correlations with cognitive impairment.

Conclusions

Compared to PD noncarriers, GBA-PD display more severe instrumental autonomic abnormalities, which may be underestimated by purely clinical measures, despite their relevance on morbidity and mortality. This supports the necessity of implementing instrumental autonomic assessment in all GBA-PD, regardless of clinically overt symptoms.

The Association between Parkinson's Disease and Congestive Heart Failure in Korea: A Nationwide Longitudinal Cohort Study

The purpose of this nationwide longitudinal follow-up study is to investigate the relationship between Parkinson's disease (PD) and congestive heart failure (CHF) patients in Korea. Patient data were collected using the National Health Insurance Service (NHIS) Health Screening (HEALS) cohort. The International Classification of Diseases 10-CM code G-20 distinguished 6475 PD patients who were enrolled in the PD group. After removing 1039 patients who were not hospitalized or attended an outpatient clinic less than twice, the total number of participants was reduced to 5436 individuals. Then, 177 patients diagnosed before 1 January 2004 were removed for relevancy, leaving us with 5259 PD patients. After case-control matching was completed using 1:5 age- and gender-coordinated matching, 26,295 people were chosen as part of the control group. The Cox proportional hazards regression analysis and the Kaplan-Meier technique were used to assess the risk of CHF in patients with Parkinson's disease. After controlling for age and gender, the hazard ratio of CHF in the PD group was 5.607 (95% confidence interval (CI), 4.496-6.993). After that, the hazard ratio of CHF in the PD group was modified against for comorbid medical disorders, resulting in a value of 5.696 (95% CI, 4.566-7.107). In subgroup analysis, CHF incidence rates were significantly increased in the PD group compared to the control group (males and females; aged ≥ 65 and <65; the non-diabetes and diabetes, hypertension and non-hypertension, and dyslipidemia and non-dyslipidemia subgroups). This nationwide longitudinal study shows a higher incidence rate of CHF in PD patients.

Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram

Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months-1 year, 6 months-3 years, and 6 months-5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

The role of the autonomic nervous system in cerebral blood flow regulation in stroke: A review

Stroke is a pathophysiological condition which results in alterations in cerebral blood flow (CBF). The mechanism by which the brain maintains adequate CBF in presence of fluctuating cerebral perfusion pressure (CPP) is known as cerebral autoregulation (CA). Disturbances in CA may be influenced by a number of physiological pathways including the autonomic nervous system (ANS). The cerebrovascular system is innervated by adrenergic and cholinergic nerve fibers. The role of the ANS in regulating CBF is widely disputed owing to several factors including the complexity of the ANS and cerebrovascular interactions, limitations to measurements, variation in methods to assess the ANS in relation to CBF as well as experimental approaches that can or cannot provide insight into the sympathetic control of CBF. CA is known to be impaired in stroke however the number of studies investigating the mechanisms by which this occurs are limited. This literature review will focus on highlighting the assessment of the ANS and CBF via indices derived from the analyses of heart rate variability (HRV), and baroreflex sensitivity (BRS), and providing a summary of both clinical and animal model studies investigating the role of the ANS in influencing CA in stroke. Understanding the mechanisms by which the ANS influences CBF in stroke patients may provide the foundation for novel therapeutic approaches to improve functional outcomes in stroke patients.

Circadian disruption and sleep disorders in neurodegeneration

Disruptions of circadian rhythms and sleep cycles are common among neurodegenerative diseases and can occur at multiple levels. Accumulating evidence reveals a bidirectional relationship between disruptions of circadian rhythms and sleep cycles and neurodegenerative diseases. Circadian disruption and sleep disorders aggravate neurodegeneration and neurodegenerative diseases can in turn disrupt circadian rhythms and sleep. Importantly, circadian disruption and various sleep disorders can increase the risk of neurodegenerative diseases. Thus, harnessing the circadian biology findings from preclinical and translational research in neurodegenerative diseases is of importance for reducing risk of neurodegeneration and improving symptoms and quality of life of individuals with neurodegenerative disorders via approaches that normalize circadian in the context of precision medicine. In this review, we discuss the implications of circadian disruption and sleep disorders in neurodegenerative diseases by summarizing evidence from both human and animal studies, focusing on the bidirectional links of sleep and circadian rhythms with prevalent forms of neurodegeneration. These findings provide valuable insights into the pathogenesis of neurodegenerative diseases and suggest a promising role of circadian-based interventions.

Neurogenic Supine Hypertension and Cardiovascular Autonomic Dysfunction in Patients with Parkinson's Disease

Background

Natural history and disease progression in patients with Idiopathic Parkinson's Disease (PD) is quite heterogeneous. Autonomic dysfunction occurs commonly among Idiopathic PD patients. Heart rate variability and ambulatory blood pressure monitoring are used to assess cardiac autonomic dysfunction. The prevalence and magnitude of supine hypertension in Indian PD patients has not been studied to date. The present study aimed to record cardiovascular autonomic functions and supine hypertension in PD patients and to correlate them with the age of onset, duration and severity of the disease, and non-motor symptom burden.

Material and Methods

The cross-sectional study involved 60 PD patients. Webster rating scale was used to determine the disease severity. Non-motor symptom burden was assessed using the Non-Motor Symptom Scale (NMSS). Ambulatory blood pressure monitoring and heart rate variability parameters determined cardiac autonomic function. Supine hypertension was defined as Systolic Blood Pressure (SBP) ≥150 mmHg and/or DBP ≥90 mmHg. Less than 10% decrease or even increase in blood pressure during the night were classified as non-dippers. Pearson coefficient was used appropriately to establish correlation. P ≤ 0.05 was considered significant.

Results

Age of onset was 61.2 ± 8.7 years and duration of disease was 1.7 ± 1.1 years. Mean Webster and non-motor symptom scores were 12.7 ± 4.4 and 15.5 ± 8.0, respectively. About 50 patients (83%) were non-dipper, while 32 (53%) had supine hypertension. Low Frequency oscillations (LF) (r = 0.28), High Frequency oscillations (HF) (r = 0.29), Standard Deviation NN intervals (SDNN) (0.26), and Root Mean Squared Successive Differences of NN intervals (RMSSD) (r = 0.28) correlated significantly with non-motor symptoms scale. LF (r = -0.39), HF (r = -0.43), SDNN (-0.40), RMSSD (r = -0.41), NN50 (r = -0.38), PNN50 (r = -0.42), mean SBP (r = 0.26), and mean DBP (r = 0.33) correlated significantly with disease duration. PNN50 (r = -0.255), mean SBP (r = -0.29), and mean DBP (r = -0.27) correlated significantly with age at onset.

Conclusion

Awareness regarding neurogenic supine hypertension is needed as it occurs commonly among Indian PD patients. Heart rate variability (HRV) parameters and ambulatory blood pressure are of significant help in the detection of early cardiovascular autonomic dysfunction and correlate significantly with disease duration and non-motor symptom burden among PD patients.

Predictive Value of Exercise Blood Pressure Changes for Orthostatic Hypotension in Patients With Parkinson's Disease

BACKGROUND AND PURPOSE

Orthostatic hypotension (OH) is common in patients with Parkinson's disease (PD). Early recognition OH is required with sensitive assessments. The purpose of this study was to determine whether blood pressure (BP) changes during exercise can predict the occurrence of OH in PD.

METHODS

This prospective cohort study included 80 consecutive patients with PD. All patients agreed to participate in a baseline evaluation and cardiopulmonary exercise test (CPET). According to the initial active standing test (AST), those without OH (PD-nonOH) at baseline had their AST results followed up for 6 months. The main outcome was defined as whether patients without OH at baseline would develop OH after 6 months. Logistic regression analysis was applied to identify the relevant variables. A nomogram was constructed based on clinical features and identified variables. The concordance index (C-index) and area under the receiver operating characteristic curve (AUC) were used to evaluate the accuracy and predictive ability of the nomogram, respectively.

RESULTS

CPET results indicated that peak load, peak heart rate, heart rate recovery at 1 min, and systolic BP change (ΔSBP) were lower in those with OH than in the PD-nonOH group (p<0.05) at baseline. Logistic regression analysis indicated that peak load and ΔSBP during CPET had significant effects on OH (p<0.05). Age, sex, peak load, and ΔSBP were used to construct the nomogram model (C-index=0.761). The prediction model had an AUC of 0.782 (95% confidence interval=0.649-0.889) and a specificity and sensitivity of 70.0% and 81.8%, respectively.

CONCLUSIONS

This study has identified predictive factors for OH development in patients with PD. CPET could be used as a complementary examination to identify patients at a high risk of OH.

Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

Impact of Equine-Assisted Interventions on Heart Rate Variability in Two Participants with 22q11.2 Deletion Syndrome: A Pilot Study

People with disabilities due to genetic origin often present high levels of stress: non-pharmacological interventions such as Equine-Assisted Interventions (EAI) may be a useful strategy. The objective of this pilot study was to evaluate stress levels in two participants with 22q11.2 deletion syndrome diagnosis, immediately after carrying out the EAI. A single case experimental design methodology was chosen due to the small sample size. Two participants with 22q11.2 Deletion Syndrome, a rare disease, with different comorbidities were included. The present study considered the EAI as the independent variable while the Heart Rate Variability (HRV) represented the dependent one, as HRV is considered an indicator of stress level. Measurements were performed before and after carrying out the interventions. The results showed an HRV increase in one of the participants and an increase in the arousal level evidenced by a decrease in his HRV. After having carried out the program, EAI seems to cause an impact on the activation level of the participants depending on the typology and nature of the intervention. However, these results should be treated with caution due to the small sample size. This study is a pilot to test the feasibility of the proposed interventions on the variable under study.

Circadian rhythms in neurodegenerative disorders

Endogenous biological clocks, orchestrated by the suprachiasmatic nucleus, time the circadian rhythms that synchronize physiological and behavioural functions in humans. The circadian system influences most physiological processes, including sleep, alertness and cognitive performance. Disruption of circadian homeostasis has deleterious effects on human health. Neurodegenerative disorders involve a wide range of symptoms, many of which exhibit diurnal variations in frequency and intensity. These disorders also disrupt circadian homeostasis, which in turn has negative effects on symptoms and quality of life. Emerging evidence points to a bidirectional relationship between circadian homeostasis and neurodegeneration, suggesting that circadian function might have an important role in the progression of neurodegenerative disorders. Therefore, the circadian system has become an attractive target for research and clinical care innovations. Studying circadian disruption in neurodegenerative disorders could expand our understanding of the pathophysiology of neurodegeneration and facilitate the development of novel, circadian-based interventions for these disabling disorders. In this Review, we discuss the alterations to the circadian system that occur in movement (Parkinson disease and Huntington disease) and cognitive (Alzheimer disease and frontotemporal dementia) neurodegenerative disorders and provide directions for future investigations in this field.

Heart Rate Variability Analyses in Parkinson's Disease: A Systematic Review and Meta-Analysis

Recent evidence suggests that the vagus nerve and autonomic dysfunction play an important role in the pathogenesis of Parkinson's disease. Using heart rate variability analysis, the autonomic modulation of cardiac activity can be investigated. This meta-analysis aims to assess if analysis of heart rate variability may indicate decreased parasympathetic tone in patients with Parkinson's disease. The MEDLINE, EMBASE and Cochrane Central databases were searched on 31 December 2020. Studies were included if they: (1) were published in English, (2) analyzed idiopathic Parkinson's disease and healthy adult controls, and (3) reported at least one frequency- or time-domain heart rate variability analysis parameter, which represents parasympathetic regulation. We included 47 studies with 2772 subjects. Random-effects meta-analyses revealed significantly decreased effect sizes in Parkinson patients for the high-frequency spectral component (HFms²) and the short-term measurement of the root mean square of successive normal-to-normal interval differences (RMSSD). However, heterogeneity was high, and there was evidence for publication bias regarding HFms². There is some evidence that a more advanced disease leads to an impaired parasympathetic regulation. In conclusion, short-term measurement of RMSSD is a reliable parameter to assess parasympathetically impaired cardiac modulation in Parkinson patients. The measurement should be performed with a predefined respiratory rate.

Short-term deceleration capacity of heart rate: a sensitive marker of cardiac autonomic dysfunction in idiopathic Parkinson's disease

PURPOSE

Cardiac autonomic dysfunction in idiopathic Parkinson's disease (PD) manifests as reduced heart rate variability (HRV). In the present study, we explored the deceleration capacity of heart rate (DC) in patients with idiopathic PD, an advanced HRV marker that has proven clinical utility.

METHODS

Standard and advanced HRV measures derived from 7-min electrocardiograms in 20 idiopathic PD patients and 27 healthy controls were analyzed. HRV measures were compared using regression analysis, controlling for age, sex, and mean heart rate.

RESULTS

Significantly reduced HRV was found only in the subcohort of PD patients older than 60 years. Low- frequency power and global HRV measures were lower in patients than in controls, but standard beat-to-beat HRV markers (i.e., rMSSD and high-frequency power) were not significantly different between groups. DC was significantly reduced in the subcohort of PD patients older than 60 years compared to controls.

CONCLUSIONS

Deceleration-related oscillations of HRV were significantly reduced in the older PD patients compared to healthy controls, suggesting that short-term DC may be a sensitive marker of cardiac autonomic dysfunction in PD. DC may be complementary to traditional markers of short-term HRV for the evaluation of autonomic modulation in PD. Further study to examine the association between DC and cardiac adverse events in PD is needed to clarify the clinical relevance of DC in this population.

A Multi-Sensor Wearable System for the Quantitative Assessment of Parkinson's Disease

The quantitative characterization of movement disorders and their related neurophysiological signals is important for the management of Parkinson’s disease (PD). The aim of this study is to develop a novel wearable system enabling the simultaneous measurement of both motion and other neurophysiological signals in PD patients. We designed a wearable system that consists of five motion sensors and three electrophysiology sensors to measure the motion signals of the body, electroencephalogram, electrocardiogram, and electromyography, respectively. The data captured by the sensors are transferred wirelessly in real time, and the outcomes are analyzed and uploaded to the cloud-based server automatically. We completed pilot studies to (1) test its validity by comparing outcomes to the commercialized systems, and (2) evaluate the deep brain stimulation (DBS) treatment effects in seven PD patients. Our results showed: (1) the motion and neurophysiological signals measured by this wearable system were strongly correlated with those measured by the commercialized systems (r > 0.94, p < 0.001); and (2) by completing the clinical supination and pronation frequency test, the frequency of motion as measured by this system increased when DBS was turned on. The results demonstrated that this multi-sensor wearable system can be utilized to quantitatively characterize and monitor motion and neurophysiological PD.