Relationship between afternoon napping and cognitive function in the ageing Chinese population

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

BACKGROUND

Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI).

OBJECTIVE

This study aimed to use data collected from fitness trackers to predict MCI status.

METHODS

In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status.

RESULTS

Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time.

CONCLUSIONS

Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies.

Sleep characteristics and risk of Alzheimer's disease: a systematic review and meta-analysis of longitudinal studies

BACKGROUND

Alzheimer's disease (AD) is on the rise in our aging society, making it crucial to identify additional risk factors to mitigate its increasing incidence. This systematic review and meta-analysis aimed to provide updated evidence regarding the association between sleep and AD.

METHODS

We conducted a comprehensive search of MEDLINE, EMBASE, and Web of Science databases from inception to July 2023 to identify longitudinal studies. Adjusted relative risks were pooled for each sleep characteristic, and a dose-response analysis was performed specifically for sleep duration.

RESULTS

A total of 15,278 records were initially retrieved, and after screening, 35 records were ultimately included in the final analysis. The results showed that insomnia (RR, 1.43; 95%CI, 1.17-1.74), sleep-disordered breathing (RR, 1.22; 95%CI, 1.07-1.39), as well as other sleep problems, including sleep fragmentation and sleep-related movement disorders, were associated with a higher risk of developing AD, while daytime napping or excessive daytime sleepiness (RR, 1.18; 95%CI, 1.00-1.40) only exhibited a trend toward a higher risk of AD development. Furthermore, our analysis revealed a significant association between self-reported sleep problems (RR, 1.34; 95%CI, 1.26-1.42) and the incidence of AD, whereas this association was not observed with sleep problems detected by objective measurements (RR, 1.14; 95%CI, 0.99-1.31). Moreover, both quite short sleep duration (< 4 h) and long duration (> 8 h) were identified as potential risk factors for AD.

CONCLUSIONS

Our study found the association between various types of sleep problems and an increased risk of AD development. However, these findings should be further validated through additional objective device-based assessments. Additional investigation is required to establish a definitive causal connection between sleep problems and AD.

Causal effects of sleep traits on metabolic syndrome and its components: a Mendelian randomization study

PURPOSE

Previous observational studies have suggested an association between sleep disturbance and metabolic syndrome (MetS). However, it remains unclear whether this association is causal. This study aims to investigate the causal effects of sleep-related traits on MetS using Mendelian randomization (MR).

METHODS

Single-nucleotide polymorphisms strongly associated with daytime napping, insomnia, chronotype, short sleep, and long sleep were selected as genetic instruments from the corresponding genome-wide association studies (GWAS). Summary-level data for MetS were obtained from two independent GWAS datasets. Univariable and multivariable MR analyses were conducted to investigate and verify the causal effects of sleep traits on MetS.

RESULTS

The univariable MR analysis demonstrated that genetically predicted daytime napping and insomnia were associated with increased risk of MetS in both discovery dataset (OR daytime napping = 1.630, 95% CI 1.273, 2.086; OR insomnia = 1.155, 95% CI 1.108, 1.204) and replication dataset (OR daytime napping = 1.325, 95% CI 1.131, 1.551; OR insomnia = 1.072, 95% CI 1.046, 1.099). For components, daytime napping was positively associated with triglycerides (beta = 0.383, 95% CI 0.160, 0.607) and waist circumference (beta = 0.383, 95% CI 0.184, 0.583). Insomnia was positively associated with hypertension (OR = 1.101, 95% CI 1.042, 1.162) and waist circumference (beta = 0.067, 95% CI 0.031, 0.104). The multivariable MR analysis indicated that the adverse effect of daytime napping and insomnia on MetS persisted after adjusting for BMI, smoking, drinking, and another sleep trait.

CONCLUSION

Our study supported daytime napping and insomnia were potential causal factors for MetS characterized by central obesity, hypertension, or elevated triglycerides.

Sleep disturbances and change in multiple cognitive domains among older adults: a multicenter study of five Nordic cohorts

STUDY OBJECTIVES

We examined and compared cross-sectional and longitudinal associations between self-reported sleep disturbances and various cognitive domains in five separate Nordic European longitudinal aging studies (baseline N = 5631, mean age = 77.7, mean follow-up = 4.16 years).

METHODS

Comparable sleep parameters across studies included reduced sleep duration/quality, insomnia symptoms (sleep latency, waking up at night, and early awakenings), short and long sleep duration, and daytime napping. The cognitive domains were episodic memory, verbal fluency, perceptual speed, executive functioning, and global cognition (aggregated measure). A series of mixed linear models were run separately in each study and then compared to assess the level and rate of change in cognitive functioning across each sleep disturbance parameter. Models were adjusted for age, sex, education, hypnotic usage, depressive symptoms, lifestyle factors, cardiovascular, and metabolic conditions. By using a coordinated analytic approach, comparable construct-level measurements were generated, and results from identical statistical models were qualitatively compared across studies.

RESULTS

While the pattern of statistically significant results varied across studies, subjective sleep disturbances were consistently associated with worse cognition and steeper cognitive decline. Insomnia symptoms were associated with poorer episodic memory and participants sleeping less or more than 7-8 hours had a steeper decline in perceptual speed. In addition, daytime napping (>2 hours) was cross-sectionally and longitudinally associated with all examined cognitive domains. Most observed associations were study-specific (except for daytime napping), and a majority of association estimates remained significant after adjusting for covariates.

CONCLUSION

This rigorous multicenter investigation further supports the importance of sleep disturbance, including insomnia, long and short sleep duration, and daytime napping on baseline cognitive functioning and rate of change among older adults. These sleep factors may be targeted in future lifestyle interventions to reduce cognitive decline.

Association of insomnia and daytime napping with metabolic syndrome and its components in a Korean population: an analysis of data from the Korean Genome and Epidemiology Study

OBJECTIVES

We investigated the association between metabolic syndrome (MetS) and the coexistence of insomnia and daytime napping, because limited data have been reported regarding this association.

METHODS

The study population was 8,440 participants aged 40-65 years, who were from the Korean Genome and Epidemiology Study. Self-reported information on insomnia symptoms and nap duration was used to define exposure variables. Data on waist circumference (WC), blood pressure (BP), and fasting blood glucose (FBG), triglyceride (TG), and high-density lipoprotein cholesterol levels in blood were used to define MetS. Multivariate logistic regression analysis was performed to obtain odds ratio (OR) and 95% confidence interval (CI).

RESULTS

In multivariate logistic regression analysis, the coexistence of insomnia and napping was not significantly associated with MetS. However, the insomnia and non-napping group showed higher ORs of high TG (OR, 1.19; 95% CI, 1.02 to 1.39) and high BP (OR, 1.28; 95% CI, 1.10 to 1.49) than the non-insomnia and non-napping group. The combination of non-insomnia and napping and that of insomnia and napping showed higher ORs of high TG (OR, 1.13; 95% CI, 1.00 to 1.29) and high FBG (OR, 1.59; 95% CI, 1.14 to 2.21), respectively. In analyses of insomnia symptoms, only the combination of difficulty in maintaining sleep (DMS) and non-napping showed a higher OR for MetS (OR, 1.25; 95% CI, 1.03 to 1.52) than the non-DMS and non-napping group.

CONCLUSIONS

Individuals with insomnia, particularly those who do not take naps, were disproportionately likely to have MetS components, especially TG or BP. Information on these variables may help predict individuals' vulnerability to specific MetS components.

Association between nocturnal sleep duration and midday napping and the incidence of sarcopenia in middle-aged and older adults: a 4-year longitudinal study

BACKGROUND

Identifying treatment targets for sarcopenia is a public health concern. This study aimed to examine the association of nocturnal sleep duration and midday napping with the presence of sarcopenia in middle-aged and older adults, utilizing data from the China Health and Retirement Longitudinal Study in 2011 and 2015.

METHODS

A sum of 7,926 individuals (≥40 years) took part in this study. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia. A self-reported questionnaire was used to collect data on nocturnal sleep duration and midday napping. Nocturnal sleep duration was categorized into three groups: short sleepers (<6 h), normal sleepers (6-8 h), and long sleepers (>8 h). Midday napping was coded as a dichotomous outcome (yes/no).

RESULTS

The incidence of sarcopenia was 5.3% during the 4-year follow-up. Short sleep duration (<6 h) was substantially linked to an increased incidence of sarcopenia (OR: 1.50, 95% CI: 1.21-1.87) as compared to nocturnal sleep length (6-8 h). Adults with midday napping had a lower risk of developing sarcopenia than non-nappers (OR: 0.78, 95% CI: 0.63-0.95). We further found that short sleepers with midday napping did not have a significantly higher risk of subsequent diagnosis of sarcopenia compared to normal sleepers without midday napping.

CONCLUSION

These findings imply that short sleep duration in middle-aged and older persons is related to an increased incidence of sarcopenia. However, the adverse effect of short sleep duration on sarcopenia can be compensated by midday napping.

An explainable machine learning based prediction model for Alzheimer's disease in China longitudinal aging study

Alzheimer's disease (AD) is the most common cause of dementia. Accurate prediction and diagnosis of AD and its prodromal stage, i.e., mild cognitive impairment (MCI), is essential for the possible delay and early treatment for the disease. In this paper, we adopt the data from the China Longitudinal Aging Study (CLAS), which was launched in 2011, and includes a joint effort of 15 institutions all over the country. Four thousand four hundred and eleven people who are at least 60 years old participated in the project, where 3,514 people completed the baseline survey. The survey collected data including demographic information, daily lifestyle, medical history, and routine physical examination. In particular, we employ ensemble learning and feature selection methods to develop an explainable prediction model for AD and MCI. Five feature selection methods and nine machine learning classifiers are applied for comparison to find the most dominant features on AD/MCI prediction. The resulting model achieves accuracy of 89.2%, sensitivity of 87.7%, and specificity of 90.7% for MCI prediction, and accuracy of 99.2%, sensitivity of 99.7%, and specificity of 98.7% for AD prediction. We further utilize the SHapley Additive exPlanations (SHAP) algorithm to visualize the specific contribution of each feature to AD/MCI prediction at both global and individual levels. Consequently, our model not only provides the prediction outcome, but also helps to understand the relationship between lifestyle/physical disease history and cognitive function, and enables clinicians to make appropriate recommendations for the elderly. Therefore, our approach provides a new perspective for the design of a computer-aided diagnosis system for AD and MCI, and has potential high clinical application value.

Association between napping and cognitive impairment: A systematic review and meta-analysis

STUDY OBJECTIVES

Increasing evidence suggests that napping is associated with cognitive impairment and dementia, but the conclusions are inconsistent. Moreover, the extent of the risk is uncertain. We therefore conducted a systematic review and meta-analysis to quantify the connection between napping and cognitive impairment.

METHODS

We performed a systematic search of PubMed, EMBASE, Web of Science, and Cochrane Library for studies that were published up to June 2023, and assessed associations between napping and cognitive impairment. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated as the effect sizes for all studies. Heterogeneity and potential publication biases were assessed.

RESULTS

A total of 4535 papers were retrieved, with 20 reports assessing the relationships between napping and cognitive impairment. Pooled analysis indicated that napping was associated with dementia (OR = 1.14; 95% CI: 1.07-1.21). Importantly, we found that those napping longer than 30, 45, and 60 min/day were 35%, 41%, and 40%, respectively, more likely to have an increased risk of cognitive impairment (30 min: OR = 1.35; 95% CI: 1.24-1.48; 45 min: OR = 1.41; 95% CI: 1.27-1.58; 60 min: OR = 1.40; 95% CI: 1.26-1.56). North America and Europe showed that associations existed between napping and cognitive impairment (North America: OR = 1.15; 95% CI: 1.04-1.27; Europe: OR = 1.13; 95% CI: 1.08-1.18).

CONCLUSIONS

This meta-analysis indicated associations between long napping durations and cognitive impairment or dementia, suggesting that longer napping might be a potential risk factor of adverse cognitive outcomes.

Daytime Napping and Cognitive Health in Older Adults: A Systematic Review

BACKGROUND

Daytime napping may improve cognitive function in older adults. However, the association can be complicated by specific features of napping and the older adult's health. This systematic review aims to synthesize the current literature on napping and cognition in older adults and provide recommendations for future research and daytime sleep practice in older adults.

METHODS

Systematic searches for relative research published between January 1995 and October 2022 were conducted at PubMed, MEDLINE, PsycINFO, and Google Scholar using keywords individually and in multiple combinations. Manual searches were performed to identify additional studies. All included studies were critically appraised by 2 authors.

RESULTS

Thirty-five studies, including 23 observational and 12 intervention studies, were reviewed. Findings from observational studies suggest a possible inverted U-shaped association between napping duration and cognitive function: short and moderate duration of naps benefited cognitive health in older adults compared with both non-napping and long or extended napping. Findings from intervention studies suggest one session of afternoon napping might improve psychomotor function and working memory, although with some inconsistency. The effect of multiple nap sessions on cognition was inconclusive due to a limited number of studies.

CONCLUSION

More rigorous research studies are needed to investigate what causes different patterns of daytime napping, the associations between these distinct patterns and cognitive function, and to determine whether interventions targeting napping patterns can improve cognition in older adults. In addition, future research needs to comprehensively assess daytime napping using a combination of measures such as sleep diary and actigraphy.

Longitudinal associations between daytime napping and cognitive function in Chinese older adults

BACKGROUND

Little was known about the longitudinal associations between daytime napping and cognitive function in China. Thus, the study aimed to explore the cross-sectional and the longitudinal relationship between daytime napping and cognitive performance in the elderly Chinese population.

METHODS

The data was from the China Health and Retirement Longitudinal Study (CHARLS). Daytime napping was self-reported. Cognitive function was assessed via a structured questionnaire in two dimensions: episodic memory and mental status. Linear regression and mixed-effect model were applied to explore the association between daytime napping and cognitive function.

RESULTS

A total of 2,875 and 2,440 participants aged over 65 years were included in the cross-sectional and the longitudinal studies, respectively. In the cross-sectional study, non-nappers and extended nappers had significantly lower global cognition scores (P<0.01), as well as significantly lower scores for episodic memory (P<0.05) and mental status (P<0.01), compared with moderate nappers. In the longitudinal analysis, no napping and extended napping were significantly associated with global cognitive decline (P<0.05) and only extended napping showed the significant association for the decline in episodic memory as well as mental status (P<0.01).

LIMITATIONS

Daytime napping duration was self-reported by participants.

CONCLUSION

The study found a longitudinal association between extended napping duration and worse cognitive function.

Both Short and Long Sleep Durations Are Associated with Poor Cognition and Memory in Chinese Adults Aged 55+ Years-Results from China Health and Nutrition Survey

We aimed to examine the associations between sleep duration and cognitive functions and memory in older Chinese adults attending the China Health and Nutrition Survey. A total of 7924 participants 55 years and older who reported their sleep duration and had a cognitive screen test in 2004, 2006, and 2015 were included in the analysis. Mixed-effects logistic regression models were used to assess the associations. A short sleep duration (≤6 h/day) and long sleep duration (≥10 h/day) were positively associated with a low global cognitive score (odds ratio-OR: 1.23, 95% CI: 1.01-1.50; OR: 1.47, 95% CI: 1.17-1.79, respectively). Both short sleepers and long sleepers had an increased risk of self-reported poor memory (OR: 1.63, 95% CI: 1.39-1.91; OR: 1.48, 95% CI: 1.25-1.74, respectively). No differences in the above associations were found for income, education, and urbanity. In conclusion, both the short and long sleep duration were associated with declined cognition and memory. Maintaining a normal sleep duration may aid in the prevention of cognitive function decline in older adults.

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.

Napping and cognitive decline: a systematic review and meta-analysis of observational studies

BACKGROUND

No clear evidence is available for the influence of napping on cognitive function in older adults. This systematic review and meta-analysis aimed to elucidate the cross-sectional and longitudinal relationships between napping and cognitive function (global cognition and memory) and to explore whether some individual characteristics and sleep characteristics can modify this relationship.

METHODS

We systematically searched Medline (via PubMed), Web of Science, and Scopus. DerSimonian and Lair and Hartung-Knapp-Sidik-Jonkman random effects methods were used to compute pooled estimates of odds ratios (ORs) and their respective 95% confidence intervals (95% CIs) for the association of global cognition and memory with napping. The mean age, the night sleep time (hours), and the percentage of women, no nappers, and people in the less night-time sleep duration category were used for meta-regressions.

RESULTS

Twenty-five studies were included in this systematic review and meta-analysis, 18 cross-sectional and seven longitudinal studies, including 95,719 participants older than 60 years. The pooled ORs from the cross-sectional analyses were 1.03 (95% CI: 1.01 to 1.06) for global cognition and 1.06 (95%: 0.90 to 1.26) for memory. The pooled ORs from the longitudinal analyses were 1.00 (95% 0.85 to 1.18) for global cognition and 1.08 (95% 0.98 to 1.19) for memory. These associations were not modified by individual or sleep characteristics.

CONCLUSION

Our data confirm the absence of association between napping and global cognition and memory regardless of the characteristics of the population. This information might be considered when providing lifestyle recommendations to adults with and without cognitive complaints.

Prospective association between sleep duration and cognitive impairment: Findings from the China Health and Retirement Longitudinal Study (CHARLS)

Objective

The association between sleep duration and cognition are inconclusive. Our study aimed to comprehensively investigate the effects of sleep duration on the risk of cognitive impairment in the middle-aged and older Chinese population.

Methods

We used the longitudinal cohort data from waves 1–4 (2011–2018) of the China Health and Retirement Longitudinal Study (CHARLS). Self-reported exposures included total sleep duration, nocturnal sleep duration, post-lunch napping, and changes in sleep duration over time according to face-to-face interviews. Cognitive function was assessed by a Chinese version of the Modified Mini-Mental State Examination (MMSE).

Results

A total of 7,342 eligible participants were included. The mean age was 61.5 ± 6.5 years, and 48.9% (3,588/7,342) were male. We identified a U-shaped association of total sleep duration as well as nocturnal sleep duration with the risk of cognitive impairment. People with 7–8 h of total sleep duration and 6–7 h of nocturnal sleep had the lowest risk of cognitive impairment. Further results showed that post-lunch napping within 2 h was beneficial to cognition and 60 min was optimal. Moreover, analyses of changes in sleep duration further supported that sleeping less or more was harmful to cognition. Notably, those “excessive-change” sleepers (from ≤6 to ≥9 h, or from ≥9 to ≤6 h) had more risks.

Conclusions

Keeping 7–8 h per day was related to the lowest risk of cognitive impairment in midlife and late life, and an optimal post-lunch napping was 60 min for these stable sleepers. Especially, excessive changes in sleep duration over time led to poorer cognition. Our work highlights the importance of optimal sleep habits to cognitive function. The self-reported sleep measures limited our findings, and further studies are needed for verification.

Evaluating the Bidirectional Causal Association Between Daytime Napping and Alzheimer’s Disease Using Mendelian Randomization

Background: Until now, both cross-sectional and longitudinal studies have identified controversial findings about the association between daytime napping and Alzheimer’s disease (AD) or cognitive decline. Therefore, it remains unclear about the causal association between daytime napping and AD or cognitive decline. Objective: We aim to investigate the causal association between daytime napping and AD. Methods: Here, we conduct a bidirectional Mendelian randomization (MR) analysis to investigate the causal association between daytime napping and AD using large-scale GWAS datasets from daytime napping including 452,633 individuals of European ancestry and AD including 35,274 AD and 59,163 controls of European ancestry. A total of five MR methods are selected including inverse-variance weighted (IVW), weighted median, MR-Egger, MR-PRESSO, and contamination mixture method. Results: MR analysis highlights significant causal association of AD with daytime napping using IVW (beta = -0.006, 95% CI [–0.009, –0.002], p = 2.00E-03), but no significant causal association of daytime napping with AD using IVW (OR = 0.76, 95% CI 0.53-1.10, p = 1.40E-01). Conclusion: Our bidirectional MR analysis demonstrates the causal effect of AD on daytime napping. However, there is no causal effect of daytime napping on AD. Our current findings are consistent with recent evidence from other MR studies that highlight little evidence supporting a causal effect of sleep traits on AD and support the causal effect of AD on sleep traits.

Afternoon Napping Durations in Chinese Population Over 60 Years Old: Longitudinal Associations With Cognitive Performance

Introduction

Previous studies indicated inconsistent associations between daytime napping and cognitive decline. This study aimed to examine the associations between self-reported changes in napping and longitudinal cognitive performance.

Methods

A national representative sample of 4,024 participants over 60 was obtained from the China Health and Retirement Longitudinal Study from 2011 to 2018. Afternoon napping and potential factors were collected by a questionnaire. Cognitive performance was assessed on three aspects. The generalized additive models and generalized estimating equations were used to examine relationships between daytime napping and longitudinal global cognition, and generalized linear models were used to examine the longitudinal associations between change in napping at four waves and cognition in wave 4.

Results

After controlling the potential confounders, participants with afternoon napping were significantly related to better global cognition than no nappers at baseline. A change from short napping to no/long napping was associated with worse cognitive performance (β = −2.593, P < 0.001). A move from no napping to short/long napping was also associated with lower cognition scores (β = −0.694, P < 0.001). For participants with moderate napping, a >30 min increase (β = −1.558, P < 0.001) in afternoon napping was associated with worse cognitive function in wave 4.

Conclusion

We observed that adults over 60 years old with napping <30 min per day may be at lower risk of cognitive decline. Change in napping, especially a move to extreme napping would be a risk marker underlying health conditions that impact cognition or go along with cognitive decline.

Association of Poor Sleep Burden in Middle Age and Older Adults With Risk for Delirium During Hospitalization

BACKGROUND

Delirium is a distressing neurocognitive disorder recently linked to sleep disturbances. However, the longitudinal relationship between sleep and delirium remains unclear. This study assessed the associations of poor sleep burden, and its trajectory, with delirium risk during hospitalization.

METHODS

About 321 818 participants from the UK Biobank (mean age 58 ± 8 years [SD]; range 37-74 years) reported (2006-2010) sleep traits (sleep duration, excessive daytime sleepiness, insomnia-type complaints, napping, and chronotype-a closely related circadian measure for sleep timing), aggregated into a sleep burden score (0-9). New-onset delirium (n = 4 775) was obtained from hospitalization records during a 12-year median follow-up. About 42 291 (mean age 64 ± 8 years; range 44-83 years) had repeat sleep assessment on average 8 years after their first.

RESULTS

In the baseline cohort, Cox proportional hazards models showed that moderate (aggregate scores = 4-5) and severe (scores = 6-9) poor sleep burden groups were 18% (hazard ratio = 1.18 [95% confidence interval: 1.08-1.28], p < .001) and 57% (1.57 [1.38-1.80], p < .001), more likely to develop delirium, respectively. The latter risk magnitude is equivalent to 2 additional cardiovascular risks. These findings appeared robust when restricted to postoperative delirium and after exclusion of underlying dementia. Higher sleep burden was also associated with delirium in the follow-up cohort. Worsening sleep burden (score increase ≥2 vs no change) further increased the risk for delirium (1.79 [1.23-2.62], p = .002) independent of their baseline sleep score and time lag. The risk was highest in those younger than 65 years at baseline (p for interaction <.001).

CONCLUSION

Poor sleep burden and worsening trajectory were associated with increased risk for delirium; promotion of sleep health may be important for those at higher risk.

Effects of surgery and general anaesthesia on sleep-wake timing: CLOCKS observational study

Surgery and general anaesthesia have the potential to disturb the body’s circadian timing system, which may affect postoperative outcomes. Animal studies suggest that anaesthesia could induce diurnal phase shifts, but clinical research is scarce. We hypothesised that surgery and general anaesthesia would result in peri‐operative changes in diurnal sleep–wake patterns in patients. In this single‐centre prospective cohort study, we recruited patients aged ≥18 years scheduled for elective surgery receiving ≥30 min of general anaesthesia. The Munich Chronotype Questionnaire and Pittsburgh Sleep Quality Index were used to determine baseline chronotype, sleep characteristics and sleep quality. Peri‐operative sleeping patterns were logged. Ninety‐four patients with a mean (SD) age of 52 (17) years were included; 56 (60%) were female. The midpoint of sleep (SD) three nights before surgery was 03.33 (55 min) and showed a phase advance of 40 minutes to 02.53 (67 min) the night after surgery (p < 0.001). This correlated with the midpoint of sleep three nights before surgery and was not associated with age, sex, duration of general anaesthesia or intra‐operative dexamethasone use. Peri‐operatively, patients had lower subjective sleep quality and worse sleep efficiency. Disruption started from one night before surgery and did not normalise until 6 days after surgery. We conclude that there is a peri‐operative phase advance in midpoint of sleep, confirming our hypothesis that surgery and general anaesthesia disturb the circadian timing system. Patients had decreased subjective sleep quality, worse sleep efficiency and increased daytime fatigue.