Associations between self-reported sleep characteristics and incident mild cognitive impairment: The Heinz Nixdorf Recall Cohort Study

The prevalence of sleep deprivation and its impact among medical officers in a tertiary hospital, a cross-sectional study from Malaysia

Sleep deprivation (SD), defined as an inability to get a minimum of 7 hours of regular sleep at night is a serious health problem that impacts the performance of medical professionals. This study aims to determine the impact of sleep deprivation on perceived performance among medical officers (MOs). A cross-sectional study design involved 231 MOs from six disciplines in Hospital Tuanku Ja'afar, a tertiary center in the south of Malaysia. A self-administered questionnaire was introduced in the English language. The questionnaire involved the sociodemographic characteristics; job-related factors, and the Sleep Deprivation Impact Scale (SDIS). The SDIS is a 12-question scale, rated on a 5-point Likert scale from strongly disagree to strongly agree. A higher SDIS score reflected a higher impact of sleep deprivation. A total of 206 MOs returned the completed questionnaire yielding a response rate of 89.17%. The mean age of respondents was 31.68 (±3.49) years. Most of the respondents were female, of Malay ethnicity, and married. More than three-quarters (78.64%) reported sleep deprivation. Being less effective in communication and formulating diagnosis (3 (1.01) vs 2.5 (1.15),p = 0.005); taking longer time to do things (3.44 (1.07) vs 2.8 (1.34),p = 0.001); and feeling unsafe while driving (3.56 (1.25) vs 2.93 (1.55),p = 0.006) manifested significantly higher mean among sleep-deprived respondents. In conclusion, sleep deprivation is a prevalent problem; that adversely affects crucial functioning domains that may endanger patients and healthcare providers alike. Radical countermeasures are required to ensure satisfactory sleep duration and address areas jeopardizing MO safety.

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.

Association of obstructive sleep apnea and sleep quality with cognitive function: a study of middle-aged and elderly persons in India

INTRODUCTION

Symptoms of obstructive sleep apnea (OSA) and poor sleep quality affect around one in ten people in India. We aimed to determine if OSA symptoms and poor sleep quality are independently associated with cognition in middle-aged and elderly urban Indian populations.

METHODS

We studied the cross-sectional association between OSA symptoms (by Berlin Questionnaire), poor sleep quality (by Pittsburgh Sleep Quality Index), and cognitive function in adults ≥ 50 years. Using a standard neuropsychological battery for cognitive function, a G-factor was derived as the first rotated principal component assessing domains of information processing, memory, and executive function. The associations of exposures with cognitive measures were modeled using linear regression, adjusted for metabolic risk factors, lifestyle factors, and psychosocial problems, followed by stratified analysis by decadal age group.

RESULTS

A total of 7505 adults were enrolled. Excluding those with MMSE < 26 (n 710), of 6795 individuals (49.2% women), mean (SD) age 64.2 (9.0) years, 38.3% had high risk of OSA symptoms, and 15.9% had poor sleep quality. OSA symptoms were negatively associated with cognitive domains of information processing (adjusted beta coefficient of z-score - 0.02, p-value 0.006), memory (- 0.03, 0.014), and G-factor (- 0.11, 0.014) in full-model. Stratified analysis by age group showed significant adverse effects of OSA symptoms on cognition for middle-aged people (50-60 years) (- 0.26, 0.001), but not in later age groups. Poor sleep quality was also associated with lower cognitive scores for G-factor (- 0.48, < 0.001), memory (- 0.08, 0.005), and executive domains (- 0.12, < 0.001), but not with information domain.

CONCLUSION

The findings suggest that both symptoms of OSA and poor sleep quality have a direct adverse impact on cognition in an Indian setting. A modest effect of age on the relationship of OSA and cognition was also observed.

Associations of sleep disorders with all-cause MCI/dementia and different types of dementia - clinical evidence, potential pathomechanisms and treatment options: A narrative review

Due to worldwide demographic change, the number of older persons in the population is increasing. Aging is accompanied by changes of sleep structure, deposition of beta-amyloid (Aß) and tau proteins and vascular changes and can turn into mild cognitive impairment (MCI) as well as dementia. Sleep disorders are discussed both as a risk factor for and as a consequence of MCI/dementia. Cross-sectional and longitudinal population-based as well as case-control studies revealed sleep disorders, especially sleep-disorderded breathing (SDB) and excessive or insufficient sleep durations, as risk factors for all-cause MCI/dementia. Regarding different dementia types, SDB was especially associated with vascular dementia while insomnia/insufficient sleep was related to an increased risk of Alzheimer's disease (AD). Scarce and still inconsistent evidence suggests that therapy of sleep disorders, especially continuous positive airway pressure (CPAP) in SDB, can improve cognition in patients with sleep disorders with and without comorbid dementia and delay onset of MCI/dementia in patients with sleep disorders without previous cognitive impairment. Regarding potential pathomechanisms via which sleep disorders lead to MCI/dementia, disturbed sleep, chronic sleep deficit and SDB can impair glymphatic clearance of beta-amyloid (Aß) and tau which lead to amyloid deposition and tau aggregation resulting in changes of brain structures responsible for cognition. Orexins are discussed to modulate sleep and Aß pathology. Their diurnal fluctuation is suppressed by sleep fragmentation and the expression suppressed at the point of hippocampal atrophy, contributing to the progression of dementia. Additionally, sleep disorders can lead to an increased vascular risk profile and vascular changes such as inflammation, endothelial dysfunction and atherosclerosis which can foster neurodegenerative pathology. There is ample evidence indicating that changes of sleep structure in aging persons can lead to dementia and also evidence that therapy of sleep disorder can improve cognition. Therefore, sleep disorders should be identified and treated early.

Sleep Quality and Cognitive Abilities in the Greek Cohort of Epirus Health Study

Purpose

Sleep is essential to all human body functions as well as brain functions. Inadequate sleep quantity and poor sleep quality have been shown to directly affect cognitive functioning and especially memory. The primary aim of the present study was to investigate the association of sleep quality with cognitive abilities cross-sectionally in a middle-aged Greek population and secondarily to examine this association prospectively in a smaller group of these participants.

Patients and Methods

A total of 2112 healthy adults aged 25-70 years (mean: 46.7±11.5) from the Epirus Health Study cohort were included in the analysis and 312 of them participated in secondary prospective analysis. Sleep quality was measured by the Pittsburgh Sleep Quality Index (PSQI) scale and cognition was assessed in primary cross-sectional analyses with three neuropsychological tests, namely the Verbal Fluency test, the Logical Memory test and the Trail Making test, and in secondary prospective analyses with online versions of Posner cueing task, an emotional recognition task, the Corsi block-tapping task and the Stroop task. Statistical analysis was performed using multivariable linear regression models adjusted for age, sex, education, body mass index and alcohol consumption.

Results

Attention/processing speed was the only cognitive domain associated cross-sectionally with PSQI score. Specifically, participants with better self-reported sleep quality performed faster on the Trail Making Test - Part A (β= 0.272 seconds, 95% CI 0.052, 0.493).

Conclusion

Further studies are needed to clarify the association of sleep quality with cognition, especially in middle-aged people that are still in productive working years.

The Relation of Sleep Characteristics and Cognitive Impairment in Community-Dwelling Middle-Aged and Older Adults: Ardakan Cohort Study on Aging (ACSA)

Introduction

The rise in the elderly population has brought attention to mild cognitive impairment (MCI). Sleep disorders also affect many older adults, indicating an important area of research for disturbed sleep and faster brain aging. This population-based study aimed to investigate the association of several sleep indicators with cognitive performance.

Methods

This cross-sectional study focused on adults over 50 in the Ardakan Cohort Study on Aging (ACSA). MCI was evaluated using the Mini-Mental State Examination (MMSE) and the Abbreviated Mental Test score (AMTS) in literate and illiterate individuals. Sleep characteristics were collected using the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale, and Berlin questionnaire. The logistic regression models were used to analyze the data.

Results

Overall, 3,380 literate and 1,558 illiterate individuals were included. In both groups, participants with MCI had a significantly higher PSQI global score (p < 0.05). Also, among the literate individuals, a significantly higher risk of having sleep-disordered breathing and poor sleep quality was observed in participants with MCI (p < 0.05). In illiterate individuals, higher sleep latency than 15 min increased odds of MCI (p < 0.05). However, after adjusting for all variables, only literate individuals with a sleep duration of more than 8 h had 66 percent increased odds of having MCI (p = 0.033).

Conclusion

Sleep duration might be associated with cognitive function in the older Iranian population. Our findings underscore the importance of considering sleep patterns in relation to cognitive health.

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.

Neurologic Disorders in Women and Sleep

Sleep disorders in women remain underrecognized and underdiagnosed mainly because of gender bias in researching and characterizing sleep disorders in women. Symptoms of common sleep disorders are frequently missed in the general female population and are expected to be further overlooked because of overlapping symptoms in women with neurologic disorders. Given the bidirectional relationship with sleep and neurologic disorders, it remains critical to be aware of the presentation and impact of sleep disorders in this patient population. This article reviews available data on sleep disorders in women with neurologic disorders and discusses their distinctive features.

Associations between self-reported sleep patterns and health, cognition and amyloid measures: results from the Wisconsin Registry for Alzheimer's Prevention

Previous studies suggest associations between self-reported sleep problems and poorer health, cognition, Alzheimer's disease pathology and dementia-related outcomes. It is important to develop a deeper understanding of the relationship between these complications and sleep disturbance, a modifiable risk factor, in late midlife, a time when Alzheimer's disease pathology may be accruing. The objectives of this study included application of unsupervised machine learning procedures to identify distinct subgroups of persons with problematic sleep and the association of these subgroups with concurrent measures of mental and physical health, cognition and PET-identified amyloid. Dementia-free participants from the Wisconsin Registry for Alzheimer's Prevention (n = 619) completed sleep questionnaires including the Insomnia Severity Index, Epworth Sleepiness Scale and Medical Outcomes Study Sleep Scale. K-means clustering analysis identified discrete sleep problem groups who were then compared across concurrent health outcomes (e.g. depression, self-rated health and insulin resistance), cognitive composite indices including episodic memory and executive function and, in a subset, Pittsburgh Compound B PET imaging to assess amyloid burden. Significant omnibus tests (P < 0.05) were followed with pairwise comparisons. Mean (SD) sample baseline sleep assessment age was 62.6 (6.7). Cluster analysis identified three groups: healthy sleepers [n = 262 (42.3%)], intermediate sleepers [n = 229 (37.0%)] and poor sleepers [n = 128 (20.7%)]. All omnibus tests comparing demographics and health measures across sleep groups were significant except for age, sex and apolipoprotein E e4 carriers; the poor sleepers group was worse than one or both of the other groups on all other measures, including measures of depression, self-reported health and memory complaints. The poor sleepers group had higher average body mass index, waist-hip ratio and homeostatic model assessment of insulin resistance. After adjusting for covariates, the poor sleepers group also performed worse on all concurrent cognitive composites except working memory. There were no differences between sleep groups on PET-based measures of amyloid. Sensitivity analyses indicated that while different clustering approaches resulted in different group assignments for some (predominantly the intermediate group), between-group patterns in outcomes were consistent. In conclusion, distinct sleep characteristics groups were identified with a sizable minority (20.7%) exhibiting poor sleep characteristics, and this group also exhibited the poorest concurrent mental and physical health and cognition, indicating substantial multi-morbidity; sleep group was not associated with amyloid PET estimates. Precision-based management of sleep and related factors may provide an opportunity for early intervention that could serve to delay or prevent clinical impairment.

Insomnia and risk of dementia in a large population-based study with 11-year follow-up: The HUNT study

Despite evidence suggesting that insomnia is associated with the risk of dementia and cognitive dysfunction, studies have shown mixed results. Dementia has a long prodromal phase, and studies with long follow-up are required to avoid reverse causality. In our 11-year follow-up study, we assessed whether probable insomnia disorder (PID) based on diagnostic criteria, and insomnia symptoms were associated with risk of all-cause dementia, Alzheimer's disease (AD) and cognition, measured with the Montreal Cognitive Assessment scale. We also examined if Apolipoprotein E genotype modified any associations with dementia through interaction. We analysed data from 7492 participants in the Norwegian Trøndelag Health Study. PID was not associated with all-cause dementia (odds ratio = 1.03, 95% confidence interval = 0.74-1.43), AD (odds ratio = 1.07, 95% confidence interval = 0.71-1.60) or Montreal Cognitive Assessment score (regression coefficient = 0.37, 95% confidence interval = -0.06 to 0.80). The insomnia symptom "difficulties maintaining sleep" was associated with a lower risk of all-cause dementia (odds ratio = 0.81, 95% confidence interval = 0.67-0.98), AD (odds ratio = 0.73, 95% confidence interval = 0.57-0.93), and better Montreal Cognitive Assessment score, mean 0.40 units (95% confidence interval = 0.15-0.64). No interaction with Apolipoprotein E genotype was found. PID and insomnia symptoms did not increase the risk of dementia in our study. More research with longer follow-up is needed, and future studies should explore if the associations to dementia risk vary across insomnia subtypes.

Association of sleep quality and nap duration with cognitive frailty among older adults living in nursing homes

Background

Sleep status, including sleep quality and nap duration, may be associated with frailty and cognitive impairment in older adults. Older adults living in nursing homes may be more prone to physical and cognitive frailties. This study aimed to investigate the association between sleep quality and nap duration, and cognitive frailty among older adults living in nursing homes.

Methods

This study included 1,206 older adults aged ≥ 60 years from nursing homes in Hunan province, China. A simple frailty questionnaire (FRAIL scale) was used and Mini-Mental State Examination was conducted to assess physical frailty and cognitive impairment, respectively, to confirm cognitive frailty. The Pittsburgh Sleep Quality Index was used to assess the sleep quality. Nap duration was classified as follows: no, short (≤30 min), and long (>30 min) napping. Multinomial logistic regression was conducted to estimate the odds ratio (OR) and 95% confidence interval (CI).

Results

The prevalence of cognitive frailty among the older adults in nursing homes was 17.5%. Approximately 60.9% of the older adults had a poor sleep quality. Among the 1,206 participants, 43.9% did not take naps, 29.1% had short naps, and 26.9% had long naps. After adjusting for all covariates, poor sleep quality (OR 2.53; 95% CI 1.78-3.59; P < 0.001) and long nap duration (OR 1.77; 95% CI 1.19-2.64; P = 0.003) were associated with higher odds of cognitive frailty, but short nap duration (OR 0.60; 95% CI 0.40-0.89; P = 0.012) was associated with low prevalence of cognitive frailty.

Conclusion

Poor sleep quality and long nap duration are significantly associated with high risk of cognitive frailty among the older adults in nursing homes. Short nap duration was associated with low prevalence of cognitive frailty. However, these associations require further validation in older adults.

Clinical trial registration

https://osf.io/57hv8.

Gait Speed and Sleep Duration Is Associated with Increased Risk of MCI in Older Community-Dwelling Adults

This study aimed to examine the linear and nonlinear associations between sleep duration and gait speed and the risk of developing mild cognitive impairment (MCI) in community-dwelling older adults. Participants were 233 older adults who met the study inclusion criteria. The MCI diagnosis was based on medical evaluations through a clinical interview conducted by a dementia specialist. Self-reported sleep duration was evaluated using the Pittsburgh Sleep Quality Index. The usual gait speed was calculated from the time taken to walk along a 4 m walkway. Multivariate logistic regression analysis was used to calculate the odds ratio (OR) and the 95% confidence interval (95% CI) of developing MCI in relation to sleep duration and gait speed. Generalized additive models were used to examine the dose−response relationships between sleep duration, gait speed, and the risk of developing MCI. Slower gait speed (OR: 1.84, 95%; CI: 1.00−3.13) and poor sleep duration (OR: 1.76, 95%; CI: 1.00−3.35) were associated with the risk of developing MCI, compared with their optimal status. In addition, the combination of poor sleep and slower gait was associated with a higher risk of developing MCI than optimal sleep duration and gait speed (OR: 3.13, 95%; CI: 1.93−5.14). Furthermore, gait speed and sleep duration were non-linearly associated with the risk of developing MCI. These results highlight the complex interplay and synergism between sleep duration and gait abilities on the risk of developing MCI in older adults. In addition, our results suggest that slower gait speed (<1.0 m/s) and short (<330 min) and long (>480 min) sleep duration may be linked to MCI risks through underlying pathways.

Poor sleep quality is negatively associated with low cognitive performance in general population independent of self-reported sleep disordered breathing

BACKGROUND

Sleep disordered breathing (SDB) plays a significant role in both sleep quality and cognition and whether it has an impact on the relationship between above two factors remains to be clear. The study aimed to explore the association between sleep quality and cognitive performance in general population by considering influence of sleep disordered breathing (SDB).

METHODS

In this cross-sectional study, we enrolled subjects aged ≥ 18 years using a multi-stage random sampling method. Cognitive status was assessed using Mini Mental State Examination (MMSE) questionnaire, sleep quality using Pittsburgh Sleep Quality Index (PSQI) and SDB was assessed using No-SAS scale, respectively. Multi-variable logistic regression was applied to examine the association of sleep quality and cognitive performance. Subgroup analyses were performed in different age groups, and in those with and without SDB.

RESULTS

Finally, 30,872 participants aged 47.5 ± 13.8 years with 53.5% women were enrolled, of whom 32.4% had poor sleep quality and 18.6% had low cognitive performance. Compared with good sleepers, subjects with poor sleep quality exhibited significantly higher presence of low cognitive performance (23.7% vs 16.2%, P < 0.001). Poor sleepers revealed 1.26 (95%CI: 1.16,1.36), 1.26 (1.08,1.46) and 1.25 (1.14,1.37) fold odds for low cognitive performance in general population and in subjects with and without self-reported SDB respectively. Stratified by age and SDB, the association was observed in young and middle-aged group without SDB (OR = 1.44, 95%CI: 1.30,1.59) and in the elderly group with SDB (OR = 1.30, 95%CI: 1.07,1.58).

CONCLUSIONS

Sleep quality is in a negative association with cognitive performance in general population independent of SDB, implying improvement of sleep disturbances is a potential objective of intervention strategies for cognitive protection at population level.

Relationship between Resting State Heart Rate Variability and Sleep Quality in Older Adults with Mild Cognitive Impairment

Sleep problems can be caused by psychological stress but are also related to cardiovascular and neurodegenerative diseases. Improving lifestyle behaviors, such as good sleep hygiene, can help to counteract the negative effects of neurodegenerative diseases and to improve quality of life. The purpose of this cross-sectional study was to investigate the relationship between subjectively reported measures of sleep quality (via Pittsburgh Sleep Quality Index (PSQI)) and objective measures of cardiac autonomic control (via resting state heart rate variability (HRV)) among individuals with mild cognitive impairment (MCI). The PSQI and resting state HRV data of 42 MCI participants (69.0 ± 5.5; 56-80 years) were analyzed. Nineteen of the participants reported poor sleep quality (PSQI score > 5). Good sleepers showed higher resting heart rate than bad sleepers (p = 0.037; ES = 0.670). Correlation analysis showed a significant correlation between the parameter HF nu and sleep efficiency, contrasting the expected positive association between reduced HRV and poor sleep quality in healthy and individuals with specific diseases. Otherwise, there were no significances, indicating that measures of subjective sleep quality and resting HRV were not related in the present sample of MCI participants. Further research is needed to better understand the complex relationship between HRV and lifestyle factors (e.g., sleep) in MCI.