Autonomic Modulation of Cardiac Activity Across Levels of Sleep Depth in Individuals With Depression and Sleep Complaints

Different heart rate variability profile during sleep in mid-later life adults with remitted early-onset versus late-onset depression

BACKGROUND

In mid-later life adults, early-onset and late-onset (i.e., onset ≥50 years) depression appear to be underpinned by different pathophysiology yet have not been examined in relation to autonomic function. Sleep provides an opportunity to examine the autonomic nervous system as the physiology changes across the night. Hence, we aimed to explore if autonomic profile is altered in mid-later life adults with remitted early-onset, late-onset and no history of lifetime depression.

METHODS

Participants aged 50-90 years (n = 188) from a specialised clinic underwent a comprehensive clinical assessment and completed an overnight polysomnography study. General Linear Models were used to examine the heart rate variability differences among the three groups for four distinct sleep stages and the wake after sleep onset. All analyses controlled for potential confounders - age, sex, current depressive symptoms and antidepressant usage.

RESULTS

For the wake after sleep onset, mid-later life adults with remitted early-onset depression had reduced standard deviation of Normal to Normal intervals (SDNN; p = .014, d = -0.64) and Shannon Entropy (p = .004, d = -0.46,) than those with no history of lifetime depression. Further, the late-onset group showed a reduction in high-frequency heart rate variability (HFn.u.) during non-rapid eye movement sleep stage 2 (N2; p = .005, d = -0.53) and non-rapid eye movement sleep stage 3 (N3; p = .009, d = -0.55) when compared to those with no lifetime history.

LIMITATIONS

Causality between heart rate variability and depression cannot be derived in this cross-sectional study. Longitudinal studies are needed to examine the effects remitted depressive episodes on autonomic function.

CONCLUSION

The findings suggest differential autonomic profile for remitted early-onset and late-onset mid-later life adults during sleep stages and wake periods. The differences could potentially serve as peripheral biomarkers in conjunction with more disease-specific markers of depression to improve diagnosis and prognosis.

High-Frequency Heart Rate Variability Is Prospectively Associated With Sleep Complaints in a Healthy Working Cohort

OBJECTIVE

Vagus nerve functioning, as indexed by high-frequency heart rate variability (HF-HRV), has been implicated in a wide range of mental and physical health conditions, including sleep complaints. This study aimed to test associations between HF-HRV measured during sleep (sleep HF-HRV) and subjective sleep complaints 4 years later.

METHODS

One hundred forty-three healthy employees (91% male; MAge = 47.8 years [time 2], SD = 8.3 years) of an industrial company in Southern Germany completed the Jenkins Sleep Problems Scale, participated in a voluntary health assessment, and were given a 24-hour ambulatory heart rate recording device in 2007. Employees returned for a health assessment and completed the Jenkins Sleep Problems Scale 4 years later.

RESULTS

Hierarchical regression analyses showed that lower sleep HF-HRV measured in 2007 was associated with higher self-reported sleep complaints 4 years later after controlling for covariates (rab,c = -0.096, b = -0.108, 95% CI, -0.298 to 0.081, ΔR2 = 0.009, p = .050).

CONCLUSIONS

These data are the first to show that lower sleep HF-HRV predicted worse sleep 4 years later, highlighting the importance of vagus nerve functioning in adaptability and health.

Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model

We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24-h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning-based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication.

Correlation between hemoglobin levels and depression in late-stage cancer patients with irritability as mediating variable

PURPOSE

In late-stage cancer, the cancer itself or the side effects of cancer treatment are known to affect the hemoglobin (Hgb) levels or emotions of patients. We to investigate the relationship between Hgb levels and depression in late-stage cancer patients and verified whether irritability has a mediating effect on this relationship.

METHOD

The research tools included a patient basic information form, the Irritability Scale-Initial Version (TISi), and the Hamilton Depression Rating Scale (HAMD). We first compared the Hgb levels, HAMD scores, and TISi scores of the cancer patients with different attributes, performed multiple hierarchical regression analysis, and then analyzed the mediating effects of TISi scores using the Sobel test.

RESULTS

In the 117 late-stage cancer patients, Hgb levels of patients with a BMI<18.5 kg/m2 were lower than those of the patients with a BMI 24.0 kg/m2. Hgb levels had a negative influence on both TISi scores (B = -2.74, p = .001) and HAMD scores (B = -0.75, p = .010). TISi scores mediated the relationship between Hgb levels and HAMD scores (Z = 2.06, p = .040).

CONCLUSIONS

Irritability is a mediating variable of the influence of Hgb levels on depression, meaning that lower Hgb levels in late-stage cancer patients may be detrimental to emotional stability, induce irritability, and thereby cause depression. Thus, in the psychological care of late-stage cancer patients, medical teams should be more vigilant in monitoring Hgb levels and anemia treatment.

Associations between sleep-related heart rate variability and both sleep and symptoms of depression and anxiety: A systematic review

There is a bidirectional relationship between poor sleep and both mood- and anxiety-related disorders, which are among leading global health concerns. Additionally, both disordered sleep and these psychiatric disorders appear to be independently associated with altered autonomic nervous system (ANS) function. We hypothesise that ANS dysregulation during sleep may explain part of the relationship between poor sleep and mood- and anxiety-related disorders. Heart rate variability (HRV) is a frequently used marker of ANS function and gives an indication of ANS input to the heart - in particular, of the relative contributions of sympathetic and parasympathetic activity. A systematic review of PubMed, Scopus and Web of Science yielded 41 studies dealing with sleep, mood- and anxiety-related disorders and sleep-related HRV. Hyperarousal during sleep, reflecting a predominance of sympathetic activation and indicative of ANS dysregulation, may be an important factor in the association between poor sleep and mood-related disorders. Longitudinal studies and mediation analyses are necessary to further understand the potential mediating role of ANS dysregulation on the relationship between poor sleep and mood- and anxiety-related disorders.

Machine learning-based classification of circadian rhythm characteristics for mild cognitive impairment in the elderly

Introduction

Using wrist-wearable sensors to ecological transient assessment may provide a more valid assessment of physical activity, sedentary time, sleep and circadian rhythm than self-reported questionnaires, but has not been used widely to study the association with mild cognitive impairment and their characteristics.

Methods

31 normal cognitive ability participants and 68 MCI participants were monitored with tri-axial accelerometer and nocturnal photo volumetric pulse wave signals for 14 days. Two machine learning algorithms: gradient boosting decision tree and eXtreme gradient boosting were constructed using data on daytime physical activity, sedentary time and nighttime physiological functions, including heart rate, heart rate variability, respiratory rate and oxygen saturation, combined with subjective scale features. The accuracy, precision, recall, F1 value, and AUC of the different models are compared, and the training and model effectiveness are validated by the subject-based leave-one-out method.

Results

The low physical activity state was higher in the MCI group than in the cognitively normal group between 8:00 and 11:00 (P < 0.05), the daily rhythm trend of the high physical activity state was generally lower in the MCI group than in the cognitively normal group (P < 0.05). The peak rhythms in the sedentary state appeared at 12:00-15:00 and 20:00. The peak rhythms of rMSSD, HRV high frequency output power, and HRV low frequency output power in the 6h HRV parameters at night in the MCI group disappeared at 3:00 a.m., and the amplitude of fluctuations decreased; the amplitude of fluctuations of LHratio nocturnal rhythm increased and the phase was disturbed; the oxygen saturation was between 90 and 95% and less than 90% were increased in all time periods (P < 0.05). The F1 value of the two machine learning algorithms for MCI classification of multi-feature data combined with subjective scales were XGBoost (78.02) and GBDT (84.04).

Conclusion

By collecting PSQI Scale data combined with circadian rhythm characteristics monitored by wrist-wearable sensors, we are able to construct XGBoost and GBDT machine learning models with good discrimination, thus providing an early warning solution for identifying family and community members with high risk of MCI.