Sleep efficiency and neurophysiological patterns in middle-aged men are associated with cognitive change over their adult life course

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 disturbance is associated with mild cognitive impairment: a community population-based cross-sectional study

BACKGROUND

Sleep is conducive to the elimination of brain metabolites and the recovery of brain function. However, the relationship between sleep disturbance and Mild Cognitive Impairment is not fully been determined.

METHODS

This was a community population-based cross-sectional study. A total of 1,443 participants from a village in the suburbs of Xi'an, China were enrolled in 2017. Sleep quality was evaluated using the Pittsburgh sleep quality index (PSQI), and sleep disturbance was defined as a PSQI score > 5. Mini-Mental State Examination (MMSE) was used to assess cognitive function and Mild Cognitive Impairment(MCI) was defined as the MMSE score less than cutoff values and meets the diagnostic criteria. Univariate and multivariate analyses were used to analyze the relationships between sleep disturbance and MCI.

RESULTS

Among 1,443 subjects, 69(4.78%) had MCI, and 830 (57.52%) had sleep disturbance. In bivariate analysis, MCI was associated with sleep disturbance (ρ = 0.094, P<0.001). In the binary logistic regression, MCI was positively associated with the sleep disturbance (OR = 2.027, 95%CI = 1.112-3.698, P = 0.021). In the internal constitution of PSQI, MCI was negatively associated with the habitual sleep efficiency (OR = 0.447, 95%CI = 0.299-0.669, P < 0.001). Compared with waking up before or at 7 am, waking up after 7 am (OR = 0.555, 95%CI = 0.309-0.995, P = 0.048), or 8 am (OR = 0.296, 95%CI = 0.097-0.902, P = 0.032) was probably more likely to have normal cognition. However, people who slept more than 8 h a day might be more likely to suffer from MCI (OR = 5.560, 95%CI = 1.419-21.789, P = 0.014).

CONCLUSION

Sleep disturbance is associated with Mild Cognitive Impairment. However, the causal relationship between them is not clear. It needs to be further studied.

Improvements in cognitive function and quantitative sleep electroencephalogram in obstructive sleep apnea after six months of continuous positive airway pressure treatment

STUDY OBJECTIVES

Untreated obstructive sleep apnea (OSA) is associated with cognitive deficits and altered brain electrophysiology. We evaluated the effect of continuous positive airway pressure (CPAP) treatment on quantitative sleep electroencephalogram (EEG) measures and cognitive function.

METHODS

We studied 167 patients with OSA (age 50 ± 13, AHI 35.0 ± 26.8) before and after 6 months of CPAP. Cognitive tests assessed working memory, sustained attention, visuospatial scanning, and executive function. All participants underwent overnight polysomnography at baseline and after CPAP. Power spectral analysis was performed on EEG data (C3-M2) in a sub-set of 90 participants. Relative delta EEG power and sigma power in NREM and EEG slowing in REM were calculated. Spindle densities (events/min) in N2 were also derived using automated spindle event detection. All outcomes were analysed as change from baseline.

RESULTS

Cognitive function across all cognitive domains improved after six months of CPAP. In our sub-set, increased relative delta power (p < .0001) and reduced sigma power (p = .001) during NREM were observed after the 6-month treatment period. Overall, fast and slow sleep spindle densities during N2 were increased after treatment.

CONCLUSIONS

Cognitive performance was improved and sleep EEG features were enhanced when assessing the effects of CPAP. These findings suggest the reversibility of cognitive deficits and altered brain electrophysiology observed in untreated OSA following six months of treatment.

The association between sleep microarchitecture and cognitive function in middle-aged and older men: a community-based cohort study

STUDY OBJECTIVES

Sleep microarchitecture parameters determined by quantitative power spectral analysis (PSA) of electroencephalograms (EEGs) have been proposed as potential brain-specific markers of cognitive dysfunction. However, data from community samples remains limited. This study examined cross-sectional associations between sleep microarchitecture and cognitive dysfunction in community-dwelling men.

METHODS

Florey Adelaide Male Ageing Study participants (n=477) underwent home-based polysomnography (PSG) (2010-2011). All-night EEG recordings were processed using PSA following artefact exclusion. Cognitive testing (2007-2010) included the inspection time task, trail-making tests A (TMT-A) and B (TMT-B), and Fuld object memory evaluation. Complete case cognition, PSG, and covariate data were available in 366 men. Multivariable linear regression models controlling for demographic, biomedical, and behavioral confounders determined cross-sectional associations between sleep microarchitecture and cognitive dysfunction overall and by age-stratified subgroups.

RESULTS

In the overall sample, worse TMT-A performance was associated with higher NREM theta and REM theta and alpha but lower delta power (all p<0.05). In men ≥65 years, worse TMT-A performance was associated with lower NREM delta but higher NREM and REM theta and alpha power (all p<0.05). Furthermore, in men ≥65 years, worse TMT-B performance was associated with lower REM delta but higher theta and alpha power (all p<0.05).

CONCLUSIONS

Sleep microarchitecture parameters may represent important brain-specific markers of cognitive dysfunction, particularly in older community-dwelling men. Therefore, this study extends the emerging community-based cohort literature on a potentially important link between sleep microarchitecture and cognitive dysfunction. Utility of sleep microarchitecture for predicting prospective cognitive dysfunction and decline warrants further investigation.

Beyond single sleep measures: A composite measure of sleep health and its associations with psychological and physical well-being in adulthood

RATIONALE

Sleep is important for many functions including body and mind restoration. Studies report the association of sleep with stress and physical deterioration, often focusing only on sleep duration; yet, sleep health needs to be understood by multiple dimensions to comprehensively capture its impact on well-being.

OBJECTIVE

This study examined cross-sectional and longitudinal associations of multidimensional sleep health with perceived stress and chronic physical conditions.

METHODS

We used a sample of 441 adults (M age = 57 years) who participated in the biomarker project of the Midlife in the United States Study. Participants provided self-report and actigraphy sleep data in 2004-2009 (T1). We created a composite score of sleep health (Range = 0-6; higher indicating more sleep problems) encompassing: actigraphy-measured regularity, timing, efficiency, duration, and self-reported satisfaction and alertness. Participants responded to the perceived stress scale and chronic physical conditions checklist at T1; chronic physical conditions were reassessed in 2013-2015 (T2).

RESULTS

Cross-sectionally, a composite score of sleep health problems was uniquely associated with higher perceived stress and more chronic conditions, explaining additional variance that each individual sleep variable did not explain. Sleep duration - one of the most commonly researched dimensions of sleep - was not associated with either perceived stress or chronic conditions. Longitudinally, for individuals who had fewer chronic conditions at T1, having more sleep health problems was associated with an increase in chronic conditions at T2. Among the multiple dimensions, sleep satisfaction was most consistently and strongly associated with the outcomes.

CONCLUSION

Findings suggest the importance of considering multiple sleep dimensions concerning psychological and physical well-being in adulthood.

Non-contact home-adapted device estimates sleep stages in middle-aged men: A preliminary study

BACKGROUND

Sleep monitoring is essential to maintain a healthy life, especially for the elderly who want to age well. Among various forms of sleep devices, the non-wearable and home-adapted device might be preferred because it can be easily used.

OBJECTIVE

In this study, we evaluated the performance of a non-contact home-adapted device compared to polysomnography (PSG), a gold standard method.

METHODS

As a preliminary study, eight subjects were recorded over fourteen nights through PSG. The non-contact home-adapted device comprised a microwave sensor, passive infrared sensor, and smartphone application. Through the device, heart rate, respiratory rate, and body movement were collected and used to estimate sleep stages. Sleep structure was labeled in four classes: wake, REM, light, and deep sleep, and were classified using a weighted k-nearest neighbor algorithm.

RESULTS

The device correctly estimated sleep structures with an overall epoch-by-epoch accuracy of 98.65% ± 0.05% based on leave-one-out cross-validation. The device showed significantly positive correlations with PSG in sleep indices including total sleep time, sleep efficiency, and wake after sleep onset.

CONCLUSIONS

Our findings demonstrate a good performance of this non-contact and home-adapted device and suggest its suitability for sleep monitoring.