Total sleep duration and daytime napping in relation to dementia detection risk: Results from the Million Women Study

Could self-reporting sleep duration become an important tool in the prediction of dementia?

INTRODUCTION

Optimal sleep duration is increasingly recognized as an important determinant of overall health, including cognitive functioning. Studies often report a U- or J-shaped relationship between sleep duration and incident dementia or cognitive deterioration, whereas long sleep, the extremes of sleep duration, and the transition to long sleep were particularly detrimental. In preclinical studies, partial or complete sleep deprivation produced inflammation, oxidative stress, as well as increased tau hyperphosphorylation and amyloid-β burden. In humans, although the findings are less pronounced, they still highlight that transitioning to an excessive sleep duration is associated with neurodegeneration. Moreover, the association between sleep duration and dementia is complex and modified by genetic, psychosocial and lifestyle factors, along with psychiatric and somatic comorbidities.

AREAS COVERED

The purpose of this perspective is to summarize the current knowledge on the association between sleep duration and dementia. It is based on a literature search for meta-analyses of prospective studies with sleep duration as an exposure and dementia as an outcome.

EXPERT OPINION

Sleep duration is a modifiable risk factor for dementia while long sleep may be an early sign of neurodegeneration. Therefore, self-reported sleep duration is an easy-to-use tool for detecting individuals who may be at risk for cognitive deterioration.

To nap or not? Evidence from a meta-analysis of cohort studies of habitual daytime napping and health outcomes

Habitual daytime napping is a common behavioral and lifestyle practice in particular countries and is often considered part of a normal daily routine. However, recent evidence suggests that the health effects of habitual daytime napping are controversial. We systematically searched PubMed, Web of Science, Embase, and Cochrane Library databases from inception to March 9, 2024, to synthesize cohort studies of napping and health outcome risk. A total of 44 cohort studies with 1,864,274 subjects aged 20-86 years (mean age 56.4 years) were included. Overall, habitual napping increased the risk of several adverse health outcomes, including all-cause mortality, cardiovascular disease, metabolic disease, and cancer, and decreased the risk of cognitive impairment and sarcopenia. Individuals with a napping duration of 30 min or longer exhibited a higher risk of all-cause mortality, cardiovascular disease, and metabolic disease, whereas those with napping durations less than 30 min had no significant risks. No significant differences in napping and health risks were observed for napping frequency, percentage of nappers, sample size, sex, age, body mass index, follow-up years, or comorbidity status. These findings indicate that individuals with a long napping duration should consider shortening their daily nap duration to 30 min or less.

Multidimensional Sleep Profiles via Machine learning and Risk of Dementia and Cardiovascular Disease

Importance

Sleep health comprises several dimensions such as duration and fragmentation of sleep, circadian activity, and daytime behavior. Yet, most research has focused on individual sleep characteristics. Studies are needed to identify sleep profiles incorporating multiple dimensions and to assess how different profiles may be linked to adverse health outcomes.

Objective

To identify actigraphy-based 24-hour sleep/circadian profiles in older men and to investigate whether these profiles are associated with the incidence of dementia and cardiovascular disease (CVD) events over 12 years.

Design

Data came from a prospective sleep study with participants recruited between 20032005 and followed until 2015-2016.

Setting

Multicenter population-based cohort study.

Participants

Among the 3,135 men enrolled, we excluded 331 men with missing or invalid actigraphy data and 137 with significant cognitive impairment at baseline, leading to a sample of 2,667 participants.

Exposures

Leveraging 20 actigraphy-derived sleep and circadian activity rhythm variables, we determined sleep/circadian profiles using an unsupervised machine learning technique based on multiple coalesced generalized hyperbolic mixture modeling.

Main Outcomes and Measures

Incidence of dementia and CVD events.

Results

We identified three distinct sleep/circadian profiles: active healthy sleepers (AHS; n=1,707 (64.0%); characterized by normal sleep duration, higher sleep quality, stronger circadian rhythmicity, and higher activity during wake periods), fragmented poor sleepers (FPS; n=376 (14.1%); lower sleep quality, higher sleep fragmentation, shorter sleep duration, and weaker circadian rhythmicity), and long and frequent nappers (LFN; n=584 (21.9%); longer and more frequent naps, higher sleep quality, normal sleep duration, and more fragmented circadian rhythmicity). Over the 12-year follow-up, compared to AHS, FPS had increased risks of dementia and CVD events (Hazard Ratio (HR)=1.35, 95% confidence interval (CI)=1.02-1.78 and HR=1.32, 95% CI=1.08-1.60, respectively) after multivariable adjustment, whereas LFN showed a marginal association with increased CVD events risk (HR=1.16, 95% CI=0.98-1.37) but not with dementia (HR=1.09, 95%CI=0.86-1.38).

Conclusion and Relevance

We identified three distinct multidimensional profiles of sleep health. Compared to healthy sleepers, older men with overall poor sleep and circadian activity rhythms exhibited worse incident cognitive and cardiovascular health. These results highlight potential targets for sleep interventions and the need for more comprehensive screening of poor sleepers for adverse outcomes.

Daytime Napping, Incident Atrial Fibrillation, and Dynamic Transitions With Dementia

Background

Associations between napping and incident atrial fibrillation (AF) remain unknown, and few studies have accounted for dynamic transitions between AF and dementia.

Objectives

The purpose of this study was to evaluate associations between napping with incident AF and the dynamic transitions of AF and dementia, as well as the mediation pathway of left ventricular (LV) size and function.

Methods

A total of 476,588 participants from UK Biobank were included. Napping frequency and other sleep behaviors were evaluated. Incident AF, dementia, and mortality were ascertained via linkage to external registry databases. LV size and function indices were obtained from cardiovascular magnetic resonance imaging phenotypes. A multistate survival analysis was conducted to examine daytime napping in relation to dynamic transitions. Weighed AF genetic risk score was calculated.

Results

Frequent daytime napping, compared to never/rarely napping, was associated with a 1.17-fold AF risk (HR: 1.17; 95% CI: 1.12-1.22), which persisted after controlling for other sleep behaviors. Genetic predisposition significantly modified associations between napping and AF (P for interaction <0.001), with stronger associations observed in those of low and moderate genetic risk. LV ejection fraction significantly mediated 26.2% (95% CI: 4.2%-74.1%) of associations between napping and AF. Frequent napping was also associated with a 1.27-fold risk of transition from AF to comorbidity of AF and dementia.

Conclusions

Our findings highlight the potential importance of screening for napping in view of the association with incident AF and dementia. Routine evaluations of the LV ejection fraction could be warranted to timely identify early indications of AF onset among habitual nappers.

Sleep duration, its change, and risk of dementia among Japanese: The Japan Public Health Center-based Prospective Study

OBJECTIVE

Previous findings on the association between sleep duration, changes in sleep duration, and long-term dementia risk were mixed. Thus, we aimed to investigate the association between midlife sleep duration, its change, and dementia.

METHODS

We recruited 41,731 Japanese (40-71 years) and documented their habitual sleep duration at baseline (1990-1994) and a 5-year follow-up survey. Changes in sleep duration were calculated as differences between baseline and 5-year measurements. We identified dementia using the Long-Term Care Insurance system (2007-2016). Hazard ratios (HRs) and 95% confidence intervals (CIs) of dementia were calculated using the area-stratified Cox model.

RESULTS

During 360,389 person-years, 4621 participants exhibited dementia. The multivariable HRs of dementia compared with 7 h of sleep were 1.13 (95% CI: 0.98-1.30) for 3-5 h, 0.93 (0.85-1.02) for 6 h, 1.06 (0.99-1.14) for 8 h, 1.13 (1.01-1.27) for 9 h, and 1.40 (1.21-1.63) for 10-12 h with a J-shaped fashion (p for linear < 0.001 and quadratic < 0.001). For its change, the HRs compared with no change were 1.02 (0.90-1.16) for decreased ≥2 h, 0.95 (0.88-1.03) for decreased 1 h, 1.00 (0.91-1.09) for increased 1 h, and 1.37 (1.20-1.58) for increased ≥2 h. The positive association for decreased sleep duration was observed in individuals with an initial sleep duration of ≤7 h, but not in those with ≥8 h (p for interaction = 0.007).

CONCLUSIONS

Long and increased sleep duration was associated with a higher risk of dementia.

A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

Understanding Sleep Regulation in Normal and Pathological Conditions, and Why It Matters

Sleep occupies a peculiar place in our lives and in science, being both eminently familiar and profoundly enigmatic. Historically, philosophers, scientists and artists questioned the meaning and purpose of sleep. If Shakespeare's verses from MacBeth depicting "Sleep that soothes away all our worries" and "relieves the weary laborer and heals hurt minds" perfectly epitomize the alleviating benefits of sleep, it is only during the last two decades that the growing understanding of the sophisticated sleep regulatory mechanisms allows us to glimpse putative biological functions of sleep. Sleep control brings into play various brain-wide processes occurring at the molecular, cellular, circuit, and system levels, some of them overlapping with a number of disease-signaling pathways. Pathogenic processes, including mood disorders (e.g., major depression) and neurodegenerative illnesses such Huntington's or Alzheimer's diseases, can therefore affect sleep-modulating networks which disrupt the sleep-wake architecture, whereas sleep disturbances may also trigger various brain disorders. In this review, we describe the mechanisms underlying sleep regulation and the main hypotheses drawn about its functions. Comprehending sleep physiological orchestration and functions could ultimately help deliver better treatments for people living with neurodegenerative diseases.