Heart Rate Variability During Nocturnal Sleep and Daytime Naps in Patients With Narcolepsy Type 1 and Type 2

Insights into vascular physiology from sleep photoplethysmography

STUDY OBJECTIVES

Photoplethysmography (PPG) in consumer sleep trackers is now widely available and used to assess heart rate variability (HRV) for sleep staging. However, PPG waveform changes during sleep can also inform about vascular elasticity in healthy persons who constitute a majority of users. To assess its potential value, we traced the evolution of PPG pulse waveform during sleep alongside measurements of HRV and blood pressure (BP).

METHODS

Seventy-eight healthy adults (50% male, median [IQR range] age: 29.5 [23.0, 43.8]) underwent overnight polysomnography (PSG) with fingertip PPG, ambulatory blood pressure monitoring, and electrocardiography (ECG). Selected PPG features that reflect arterial stiffness: systolic to diastolic distance (∆T_norm), normalized rising slope (Rslope) and normalized reflection index (RI) were derived using a custom-built algorithm. Pulse arrival time (PAT) was calculated using ECG and PPG signals. The effect of sleep stage on these measures of arterial elasticity and how this pattern of sleep stage evolution differed with participant age were investigated.

RESULTS

BP, heart rate (HR) and PAT were reduced with deeper non-REM sleep but these changes were unaffected by the age range tested. After adjusting for lowered HR, ∆T_norm, Rslope, and RI showed significant effects of sleep stage, whereby deeper sleep was associated with lower arterial stiffness. Age was significantly correlated with the amount of sleep-related change in ∆T_norm, Rslope, and RI, and remained a significant predictor of RI after adjustment for sex, body mass index, office BP, and sleep efficiency.

CONCLUSIONS

The current findings indicate that the magnitude of sleep-related change in PPG waveform can provide useful information about vascular elasticity and age effects on this in healthy adults.

The temporal relationships between sleep disturbance and autonomic dysregulation: A co-twin control study

INTRODUCTION

Sleep disturbance is associated with autonomic dysregulation, but the temporal directionality of this relationship remains uncertain. The objective of this study was to evaluate the temporal relationships between objectively measured sleep disturbance and daytime or nighttime autonomic dysregulation in a co-twin control study.

METHODS

A total of 68 members (34 pairs) of the Vietnam Era Twin Registry were studied. Twins underwent 7-day in-home actigraphy to derive objective measures of sleep disturbance. Autonomic function indexed by heart rate variability (HRV) was obtained using 7-day ECG monitoring with a wearable patch. Multivariable vector autoregressive models with Granger causality tests were used to examine the temporal directionality of the association between daytime and nighttime HRV and sleep metrics, within twin pairs, using 7-day collected ECG data.

RESULTS

Twins were all male, mostly white (96%), with mean (SD) age of 69 (2) years. Higher daytime HRV across multiple domains was bidirectionally associated with longer total sleep time and lower wake after sleep onset; these temporal dynamics were extended to a window of 48 h. In contrast, there was no association between nighttime HRV and sleep measures in subsequent nights, or between sleep measures from previous nights and subsequent nighttime HRV.

CONCLUSIONS

Daytime, but not nighttime, autonomic function indexed by HRV has bidirectional associations with several sleep dimensions. Dysfunctions in autonomic regulation during wakefulness can lead to subsequent shorter sleep duration and worse sleep continuity, and vice versa, and their influence on each other may extend beyond 24 h.

Investigation on the Prediction of Cardiovascular Events Based on Multi-Scale Time Irreversibility Analysis

Investigation of the risk factors associated with cardiovascular disease (CVD) plays an important part in the prevention and treatment of CVD. This study investigated whether alteration in the multi-scale time irreversibility of sleeping heart rate variability (HRV) was a risk factor for cardiovascular events. The D-value, based on analysis of multi-scale increments in HRV series, was used as the measurement of time irreversibility. Eighty-four subjects from an open-access database (i.e., the Sleep Heart Health Study) were included in this study. None of them had any CVD history at baseline; 42 subjects had cardiovascular events within 1 year after baseline polysomnography and were classed as the CVD group, and the other 42 subjects in the non-CVD group were age matched with those in the CVD group and had no cardiovascular events during the 15-year follow-up period. We compared D-values of sleeping HRV between the CVD and non-CVD groups and found that the D-values of the CVD group were significantly lower than those of the non-CVD group on all 10 scales, even after adjusting for gender and body mass index. Moreover, we investigated the performance of a machine learning model to classify CVD and non-CVD subjects. The model, which was fed with a feature space based on the D-values on 10 scales and trained by a random forest algorithm, achieved an accuracy of 80.8% and a positive prediction rate of 86.7%. These results suggest that the decreased time irreversibility of sleeping HRV is an independent predictor of cardiovascular events that could be used to assist the intelligent prediction of cardiovascular events.

Changes in physiological network connectivity of body system in narcolepsy during REM sleep

BACKGROUND

Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body's systemic networks.

METHOD

Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine.

RESULT

In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively.

CONCLUSION

Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.

Altered heart rate variability during sleep in mild cognitive impairment

STUDY OBJECTIVES

Cardiovascular autonomic dysfunction, as measured by short-term diurnal heart rate variability (HRV), has been reported in older adults with mild cognitive impairment (MCI). However, it is unclear whether this impairment also exists during sleep in this group. We, therefore, compared overnight HRV during sleep in older adults with MCI and those with subjective cognitive impairment (SCI).

METHODS

Older adults (n = 210) underwent overnight polysomnography. Eligible participants were characterized as multi-domain MCI or SCI. The multi-domain MCI group was comprised of amnestic and non-amnestic subtypes. Power spectral analysis of HRV was conducted on the overnight electrocardiogram during non-rapid eye movement (NREM), rapid eye movement (REM), N1, N2, N3 sleep stages, and wake periods. High-frequency HRV (HF-HRV) was employed as the primary measure to estimate parasympathetic function.

RESULTS

The MCI group showed reduced HF-HRV during NREM sleep (p = 0.018), but not during wake or REM sleep (p > 0.05) compared to the SCI group. Participants with aMCI compared to SCI had the most pronounced reduction in HF-HRV across all NREM sleep stages-N1, N2, and N3, but not during wake or REM sleep. The naMCI sub-group did not show any significant differences in HF-HRV during any sleep stage compared to SCI.

CONCLUSIONS

Our study showed that amnestic MCI participants had greater reductions in HF-HRV during NREM sleep, relative to those with SCI, suggesting potential vulnerability to sleep-related parasympathetic dysfunction. HF-HRV, especially during NREM sleep, may be an early biomarker for dementia detection.

Evaluation of sleep quality, insomnia severity and OSAS risk in primary care population: A descriptive study

PURPOSE

In our study, we aimed to evaluate the sleep quality, insomnia presence and severity, obstructive sleep apnoea syndrome (OSAS) risk of the patients who applied to family health centres and to determine the situations that might be related with these features.

METHODS

This study is a descriptive research and conducted in Ankara Güdül, Antalya Değirmenözü, Bursa Sırameşeler, Gaziantep Family Health Centre policlinics. The study population consisted of all patients over 18 years of age who were admitted to the family health centres for any reason. A 10-question questionnaire, Berlin questionnaire, Pittsburgh sleep quality questionare and insomnia severity questionare were applied by the researchers from October to December 2017 by using face-to-face interview method.

RESULTS

Two hundred nineteen nine people participated in study and 54.5% of them were women. According to the results of Pittsburgh Sleep Quality Questionare, it was found that 27.1% of the participants' sleep quality was good; according to the Berlin sleep questionnaire, 27.4% of the participants had high OSAS risk. According to Insomnia Severity Questionare, 27.1% of them had insomnia lower threshold, 15.4% had moderate insomnia and 3.7% severe insomnia.

CONCLUSIONS

In this context, it will be very effective in terms of the quality of life of patients in order to determine the conditions that disrupt sleep hygiene and to perform the necessary interventions which can be intervened in the primary healthcare institutions and the other patients to be delivered to the related upper levels.