Short Average Duration of NREM/REM Cycle Is Related to Cognitive Decline in an Elderly Cohort: An Exploratory Investigation

Association between alterations in sleep spindles and cognitive decline in persons with Parkinson's disease

BACKGROUND

Sleep macro and microstructural features have a relevant role for cognition. Although alterations in sleep macrostructure have been reported in persons with neurodegenerative disorders, including Parkinson's disease (PD), it is unknown whether there is a relationship between alterations in microstructure (sleep spindles) and global cognitive deficits in this disease.

OBJECTIVE

To explore the association between the macro and microstructure of sleep (sleep spindles) and the general cognitive state in persons with PD.

METHODS

Thirty-three patients with idiopathic PD underwent a one-night polysomnography (PSG) and a global cognitive assessment using the Montreal Cognitive Assessment (MoCA) test. PSG-based macrostructural sleep values and quantification and spectral estimation of sleep spindles were obtained.

RESULTS

We found increases in total sleep time, latency to rapid eye movement (REM) sleep, and percentage of N1 stage, as well as a decrease in percentage of REM sleep and sleep efficiency compared to values reported in healthy adults. Compared to expected values, a decrease in the number of sleep spindles was found at frontal regions. Participants with cognitive impairment showed an even lower count of sleep spindles, as well as an increase in the amplitude of underlying sigma (12-16 Hz) waves (fast spindles). When exploring MoCA subdomains, we found a consistent relationship between the number and amplitude of sleep spindles and attention capacity.

CONCLUSIONS

Decreased number and increased amplitude of sleep spindles are linked to cognitive impairment in persons with PD, especially in attention capacity. Therefore, sleep spindles characteristics could serve as prognostic indicators of cognitive deterioration in PD.

Using cross-recurrence quantification analysis to compute similarity measures for time series of unequal length with applications to sleep stage analysis

Comparing time series of unequal length requires data processing procedures that may introduce biases. This article describes, validates, and applies Cross-Recurrence Quantification Analysis (CRQA) to detect and quantify correlation and coupling among time series of unequal length without prior data processing. We illustrate and validate this application using continuous and discrete data from a model system (study 1). Then we use the method to re-analyze the Sleep Heart Health Study (SHHS), a rare large dataset comprising detailed physiological sleep measurements acquired by in-home polysomnography. We investigate whether recurrence patterns of ultradian NREM/REM sleep cycles (USC) predict mortality (study 2). CRQA exhibits better performance compared with traditional approaches that require trimming, stretching or compression to bring two time series to the same length. Application to the SHHS indicates that recurrence patterns linked to stability of USCs are associated with all-cause mortality even after controlling for other sleep parameters, health, and sociodemographics. We suggest that CRQA is a useful tool for analyzing categorical time series, where the underlying structure of the data is unlikely to result in matching data points-such as ultradian sleep cycles.

Prolonged sleep duration as a predictor of cognitive decline: A meta-analysis encompassing 49 cohort studies

Despite numerous studies have explored the association between sleep duration and cognition, the link between sleep duration trajectories and cognition remains underexplored. This systematic review aims to elucidate this correlation. We analyzed 55 studies from 14 countries, comprising 36 studies focusing on sleep duration, 20 on insomnia, and 13 on hypersomnia. A total of 10,767,085 participants were included in 49 cohort studies with a mean follow-up duration of 9.1 years. A non-linear association between sleep duration and cognitive decline was identified. Both long (risk ratio (RR):1.35, 95 % confidence intervals (CIs):1.23-1.48) and short sleep durations (RR: 1.12, 95 % CIs:1.03-1.22) were associated with an elevated risk of cognitive decline compared to moderate sleep duration. Additionally, hypersomnia (RR:1.26, 95 % CIs: 1.15-1.39) and insomnia (RR: 1.16, 95 % CIs: 1.002-1.34) were also linked to an increased risk. Moreover, prolonged sleep duration posed a higher risk of cognitive decline than stable sleep duration (RR:1.42, 95 % CIs:1.27-1.59). Importantly, transitioning from short or moderate to long sleep duration, as well as persistent long sleep duration, exhibited higher RRs for cognitive decline (RRs: 1.94, 1.40, and 1.28, respectively) compared to persistent moderate sleep duration. Our findings underscore the significance of prolonged sleep duration, alongside short and long sleep durations, with an elevated risk of cognitive decline. The association is tied to the degree of sleep duration changes. Our study highlights the importance of considering changes in sleep patterns over time, not just static sleep durations.

Sleep Biomarkers for Predicting Cognitive Decline and Alzheimer's Disease: A Systematic Review of Longitudinal Studies

BACKGROUND

Sleep disturbances are considered a hallmark of dementia, and strong evidence supports the association between alterations in sleep parameters and cognitive decline in patients with mild cognitive impairment and Alzheimer's disease (AD).

OBJECTIVE

This systematic review aims to summarize the existing evidence on the longitudinal association between sleep parameters and cognitive decline, with the goal of identifying potential sleep biomarkers of AD-related neurodegeneration.

METHODS

Literature search was conducted in PubMed, Web of Science, and Scopus databases from inception to 28 March 2023. Longitudinal studies investigating the association between baseline objectively-measured sleep parameters and cognitive decline were assessed for eligibility.

RESULTS

Seventeen studies were included in the qualitative synthesis. Sleep fragmentation, reduced sleep efficiency, reduced REM sleep, increased light sleep, and sleep-disordered breathing were identified as predictors of cognitive decline. Sleep duration exhibited a U-shaped relation with subsequent neurodegeneration. Additionally, several sleep microstructural parameters were associated with cognitive decline, although inconsistencies were observed across studies.

CONCLUSIONS

These findings suggest that sleep alterations hold promise as early biomarker of cognitive decline, but the current evidence is limited due to substantial methodological heterogeneity among studies. Further research is necessary to identify the most reliable sleep parameters for predicting cognitive impairment and AD, and to investigate interventions targeting sleep that can assist clinicians in the early recognition and treatment of cognitive decline. Standardized procedures for longitudinal studies evaluating sleep and cognition should be developed and the use of continuous sleep monitoring techniques, such as actigraphy or EEG headband, might be encouraged.

Sleep Duration and Executive Function in Adults

PURPOSE OF REVIEW

To review the literature examining the relationship between sleep and cognition, specifically examining the sub-domain of executive function. We explore the impact of sleep deprivation and the important question of how much sleep is required for optimal cognitive performance. We consider how other sleep metrics, such as sleep quality, may be a more meaningful measure of sleep. We then discuss the putative mechanisms between sleep and cognition followed by their contribution to developing dementia.

RECENT FINDINGS

Sleep duration and executive function display a quadratic relationship. This suggests an optimal amount of sleep is required for daily cognitive processes. Poor sleep efficiency and sleep fragmentation are linked with poorer executive function and increased risk of dementia during follow-up. Sleep quality may therefore be more important than absolute duration. Biological mechanisms which may underpin the relationship between sleep and cognition include brain structural and functional changes as well as disruption of the glymphatic system. Sleep is an important modifiable lifestyle factor to improve daily cognition and, possibly, reduce the risk of developing dementia. The impact of optimal sleep duration and sleep quality may have important implications for every ageing individual.

Sex differences in the effects of sleep disorders on cognitive dysfunction

Sleep is an essential physiological function that sustains human life. Sleep disorders involve problems with the quality, duration, and abnormal behaviour of sleep. Insomnia is the most common sleep disorder, followed by sleep-disordered breathing (SDB). Sleep disorders often occur along with medical conditions or other mental health conditions. Of particular interest to researchers is the role of sleep disorders in cognitive dysfunction. Sleep disorder is a risk factor for cognitive dysfunction, yet the exact pathogenesis is still far from agreement. Little is known about how sex differences influence the changes in cognitive functions caused by sleep disorders. This narrative review examines how sleep disorders might affect cognitive impairment, and then explores the sex-specific consequences of sleep disorders as a risk factor for dementia and the potential underlying mechanisms. Some insights on the direction of further research are also presented.

Associations between objectively measured sleep parameters and cognition in healthy older adults: A meta-analysis

Multiple studies have examined associations between sleep and cognition in older adults, but a majority of these depend on self-reports on sleep and utilize cognitive tests that assess overall cognitive function. The current meta-analysis involved 72 independent studies and sought to quantify associations between objectively measured sleep parameters and cognitive performance in healthy older adults. Both sleep macrostructure (e.g., sleep duration, continuity, and stages) and microstructure (e.g., slow wave activity and spindle activity) were evaluated. For macrostructure, lower restlessness at night was associated with better memory performance (r = 0.43, p = 0.02), while lower sleep onset latency was associated with better executive functioning (r = 0.28, p = 0.03). Greater relative amount of N2 and REM sleep, but not N3, positively correlated with cognitive performance. The association between microstructure and cognition in older adults was marginally significant. This relationship was moderated by age (z = 0.07, p < 0.01), education (z = 0.26, p = 0.03), and percentage of female participants (z = 0.01, p < 0.01). The current meta-analysis emphasizes the importance of considering objective sleep measures to understand the relationship between sleep and cognition in healthy older adults. These results also form a base from which researchers using wearable sleep technology and measuring behavior through computerized testing tools can evaluate their findings.

Contact-free radar recordings of body movement can reflect ultradian dynamics of sleep

This work aimed to evaluate if a contact-free radar sensor can be used to observe ultradian patterns in sleep physiology, by way of a data processing tool known as Locomotor Inactivity During Sleep (LIDS). LIDS was designed as a simple transformation of actigraphy recordings of wrist movement, meant to emphasise and enhance the contrast between movement and non-movement and to reveal patterns of low residual activity during sleep that correlate with ultradian REM/NREM cycles. We adapted the LIDS transformation for a radar that detects body movements without direct contact with the subject and applied it to a dataset of simultaneous recordings with polysomnography, actigraphy, and radar from healthy young adults (n = 12, four nights of polysomnography per participant). Radar and actigraphy-derived LIDS signals were highly correlated with each other (r > 0.84), and the LIDS signals were highly correlated with reduced-resolution polysomnographic hypnograms (r_radars  >0.80, r_actigraph  >0.76). Single-harmonic cosine models were fitted to LIDS signals and hypnograms; significant differences were not found between their amplitude, period, and phase parameters. Mixed model analysis revealed similar slopes of decline per cycle for radar-LIDS, actigraphy-LIDS, and hypnograms. Our results indicate that the LIDS technique can be adapted to work with contact-free radar measurements of body movement; it may also be generalisable to data from other body movement sensors. This novel metric could aid in improving sleep monitoring in clinical and real-life settings, by providing a simple and transparent way to study ultradian dynamics of sleep using nothing more than easily obtainable movement data.

Sleep Biomarkers Help Predict the Development of Alzheimer Disease

SUMMARY

Middle-aged or older adults who self-report sleep-wake disorders are at an increased risk for incident dementia, mild cognitive impairment, and Alzheimer disease. Dementia in people with mild cognitive impairment and Alzheimer disease who complain of sleep-wake disorders progress faster than those without sleep-wake disorders. Removal of amyloid-beta and tau tangles occurs preferentially in non-rapid eye movement 3 sleep and fragmented or insufficient sleep may lead to accumulation of these neurotoxins even in preclinical stages. Selective atrophy in the medial temporal lobe on brain MRI has been shown to predict impaired coupling of slow oscillations and sleep spindles. Impaired slow wave-spindle coupling has been shown to correlate with impaired overnight memory consolidation. Whereas, a decrease in the amplitude of 0.6 to 1 Hz slow wave activity predicts higher cortical Aβ burden on amyloid PET scans. Overexpression of the wake-promoting neurotransmitter orexin may predispose patients with mild cognitive impairment and Alzheimer disease to increased wakefulness, decreasing time they need to clear from the brain the neurotoxic accumulation of amyloid-beta and especially tau. More research exploring these relationships is needed and continuing.

Sleep and longitudinal cognitive performance in preclinical and early symptomatic Alzheimer's disease

Sleep monitoring may provide markers for future Alzheimer's disease; however, the relationship between sleep and cognitive function in preclinical and early symptomatic Alzheimer's disease is not well understood. Multiple studies have associated short and long sleep times with future cognitive impairment. Since sleep and the risk of Alzheimer's disease change with age, a greater understanding of how the relationship between sleep and cognition changes over time is needed. In this study, we hypothesized that longitudinal changes in cognitive function will have a non-linear relationship with total sleep time, time spent in non-REM and REM sleep, sleep efficiency and non-REM slow wave activity. To test this hypothesis, we monitored sleep-wake activity over 4-6 nights in 100 participants who underwent standardized cognitive testing longitudinally, APOE genotyping, and measurement of Alzheimer's disease biomarkers, total tau and amyloid-β42 in the CSF. To assess cognitive function, individuals completed a neuropsychological testing battery at each clinical visit that included the Free and Cued Selective Reminding test, the Logical Memory Delayed Recall assessment, the Digit Symbol Substitution test and the Mini-Mental State Examination. Performance on each of these four tests was Z-scored within the cohort and averaged to calculate a preclinical Alzheimer cognitive composite score. We estimated the effect of cross-sectional sleep parameters on longitudinal cognitive performance using generalized additive mixed effects models. Generalized additive models allow for non-parametric and non-linear model fitting and are simply generalized linear mixed effects models; however, the linear predictors are not constant values but rather a sum of spline fits. We found that longitudinal changes in cognitive function measured by the cognitive composite decreased at low and high values of total sleep time (P < 0.001), time in non-REM (P < 0.001) and REM sleep (P < 0.001), sleep efficiency (P < 0.01) and <1 Hz and 1-4.5 Hz non-REM slow wave activity (P < 0.001) even after adjusting for age, CSF total tau/amyloid-β42 ratio, APOE ε4 carrier status, years of education and sex. Cognitive function was stable over time within a middle range of total sleep time, time in non-REM and REM sleep and <1 Hz slow wave activity, suggesting that certain levels of sleep are important for maintaining cognitive function. Although longitudinal and interventional studies are needed, diagnosing and treating sleep disturbances to optimize sleep time and slow wave activity may have a stabilizing effect on cognition in preclinical or early symptomatic Alzheimer's disease.

Association of Rapid Eye Movement Sleep With Mortality in Middle-aged and Older Adults

Importance

Rapid eye movement (REM) sleep has been linked with health outcomes, but little is known about the relationship between REM sleep and mortality.

Objective

To investigate whether REM sleep is associated with greater risk of mortality in 2 independent cohorts and to explore whether another sleep stage could be driving the findings.

Design, Setting, and Participants

This multicenter population-based cross-sectional study used data from the Outcomes of Sleep Disorders in Older Men (MrOS) Sleep Study and Wisconsin Sleep Cohort (WSC). MrOS participants were recruited from December 2003 to March 2005, and WSC began in 1988. MrOS and WSC participants who had REM sleep and mortality data were included. Analysis began May 2018 and ended December 2019.

Main Outcomes and Measures

All-cause and cause-specific mortality confirmed with death certificates.

Results

The MrOS cohort included 2675 individuals (2675 men [100%]; mean [SD] age, 76.3 [5.5] years) and was followed up for a median (interquartile range) of 12.1 (7.8-13.2) years. The WSC cohort included 1386 individuals (753 men [54.3%]; mean [SD] age, 51.5 [8.5] years) and was followed up for a median (interquartile range) of 20.8 (17.9-22.4) years. MrOS participants had a 13% higher mortality rate for every 5% reduction in REM sleep (percentage REM sleep SD = 6.6%) after adjusting for multiple demographic, sleep, and health covariates (age-adjusted hazard ratio, 1.12; fully adjusted hazard ratio, 1.13; 95% CI, 1.08-1.19). Results were similar for cardiovascular and other causes of death. Possible threshold effects were seen on the Kaplan-Meier curves, particularly for cancer; individuals with less than 15% REM sleep had a higher mortality rate compared with individuals with 15% or more for each mortality outcome with odds ratios ranging from 1.20 to 1.35. Findings were replicated in the WSC cohort despite younger age, inclusion of women, and longer follow-up (hazard ratio, 1.17; 95% CI, 1.03-1.34). A random forest model identified REM sleep as the most important sleep stage associated with survival.

Conclusions and Relevance

Decreased percentage REM sleep was associated with greater risk of all-cause, cardiovascular, and other noncancer-related mortality in 2 independent cohorts.