Heart Rate Variability and Mild Cognitive Impairment in Parkinson's Disease

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.

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.

An Exploratory Study of Sleep-Wake Differences of Autonomic Activity in Patients with Mild Cognitive Impairment: The Role of Melatonin as a Modulating Factor

Purpose

The objective of the present study was to assess sleep-wake differences of autonomic activity in patients with mild cognitive impairment (MCI) compared to control subjects. As a post-hoc objective, we sought to evaluate the mediating effect of melatonin on this association.

Patients and Methods

A total of 22 MCI patients (13 under melatonin treatment) and 12 control subjects were included in this study. Sleep-wake periods were identified by actigraphy and 24hr-heart rate variability measures were obtained to study sleep-wake autonomic activity.

Results

MCI patients did not show any significant differences in sleep-wake autonomic activity when compared to control subjects. Post-hoc analyses revealed that MCI patients not taking melatonin displayed lower parasympathetic sleep-wake amplitude than controls not taking melatonin (RMSSD -7 ± 1 vs 4 ± 4, p = 0.004). In addition, we observed that melatonin treatment was associated with greater parasympathetic activity during sleep (VLF 15.5 ± 0.1 vs 15.1 ± 0.1, p = 0.010) and in sleep-wake differences in MCI patients (VLF 0.5 ± 0.1 vs 0.2 ± 0.0, p = 0.004).

Conclusion

These preliminary findings hint at a possible sleep-related parasympathetic vulnerability in patients at prodromal stages of dementia as well as a potential protective effect of exogenous melatonin in this population.

Association between heart rate variability and striatal dopamine depletion in Parkinson's disease

Striatal dopamine depletion is associated with not only motor symptom but also non-motor symptoms in patients with Parkinson's disease (PD). The purpose is to elucidate the relation between heart rate variability (HRV) and dopaminergic depletion in specific striatal subregions. The subjects were 84 patients with newly diagnosed untreated PD. All patients underwent striatal ¹²³I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane (¹²³I-FP-CIT) dopamine transporter single-photon emission computed tomography (DAT-SPECT). DaTQUANT software (GE Healthcare) was used as a semi-quantitative tool to analyze DAT-SPECT data. Association of HRV with dopaminergic depletion in specific striatal subregions was examined. HRV was related to dopamine depletion in the caudate and anterior putamen, especially the left side, after controlling for age, hemoglobin A1c level, disease duration, motor severity and global cognition on multiple regression analysis (left caudate p = 0.012). HRV was closely related to striatal dopamine depletion, especially in the left associative striatum, in patients with PD.