Dataset Information

The association between sleep patterns and obesity in older adults.

ABSTRACT: Reduced sleep duration has been increasingly reported to predict obesity. However, timing and regularity of sleep may also be important. In this study, the cross-sectional association between objectively measured sleep patterns and obesity was assessed in two large cohorts of older individuals.Wrist actigraphy was performed in 3053 men (mean age: 76.4 years) participating in the Osteoporotic Fractures in Men Study and 2985 women (mean age: 83.5 years) participating in the Study of Osteoporotic Fractures. Timing and regularity of sleep patterns were assessed across nights, as well as daytime napping.Greater night-to-night variability in sleep duration and daytime napping were associated with obesity independent of mean nocturnal sleep duration in both men and women. Each 1?h increase in the standard deviation of nocturnal sleep duration increased the odds of obesity 1.63-fold (95% confidence interval: 1.31-2.02) among men and 1.22-fold (95% confidence interval: 1.01-1.47) among women. Each 1?h increase in napping increased the odds of obesity 1.23-fold (95% confidence interval: 1.12-1.37) in men and 1.29-fold (95% confidence interval: 1.17-1.41) in women. In contrast, associations between later sleep timing and night-to-night variability in sleep timing with obesity were less consistent.In both older men and women, variability in nightly sleep duration and daytime napping were associated with obesity, independent of mean sleep duration. These findings suggest that characteristics of sleep beyond mean sleep duration may have a role in weight homeostasis, highlighting the complex relationship between sleep and metabolism.

SUBMITTER: Patel SR

PROVIDER: S-EPMC4110191 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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The association between sleep patterns and obesity in older adults.

Patel S R SR Hayes A L AL Blackwell T T Evans D S DS Ancoli-Israel S S Wing Y K YK Stone K L KL

International journal of obesity (2005) 20140124 9

<h4>Background</h4>Reduced sleep duration has been increasingly reported to predict obesity. However, timing and regularity of sleep may also be important. In this study, the cross-sectional association between objectively measured sleep patterns and obesity was assessed in two large cohorts of older individuals.<h4>Methods</h4>Wrist actigraphy was performed in 3053 men (mean age: 76.4 years) participating in the Osteoporotic Fractures in Men Study and 2985 women (mean age: 83.5 years) participa ...[more]

PMID: 24458262

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Project description:ImportanceSleep disturbances and clinical sleep disorders are associated with all-cause dementia and neurodegenerative conditions, but it remains unclear how longitudinal changes in sleep impact the incidence of cognitive impairment.ObjectiveTo evaluate the association of longitudinal sleep patterns with age-related changes in cognitive function in healthy older adults.Design, setting, and participantsThis cross-sectional study is a retrospective longitudinal analyses of the Seattle Longitudinal Study (SLS), which evaluated self-reported sleep duration (1993-2012) and cognitive performance (1997-2020) in older adults. Participants within the SLS were enrolled as part of a community-based cohort from the Group Health Cooperative of Puget Sound and Health Maintenance Organization of Washington between 1956 and 2020. Data analysis was performed from September 2020 to May 2023.Main outcomes and measuresThe main outcome for this study was cognitive impairment, as defined by subthreshold performance on both the Mini-Mental State Examination and the Mattis Dementia Rating Scale. Sleep duration was defined by self-report of median nightly sleep duration over the last week and was assessed longitudinally over multiple time points. Median sleep duration, sleep phenotype (short sleep, median ≤7 hours; medium sleep, median = 7 hour; long sleep, median ≥7 hours), change in sleep duration (slope), and variability in sleep duration (SD of median sleep duration, or sleep variability) were evaluated.ResultsOf the participants enrolled in SLS, only 1104 participants who were administered both the Health Behavior Questionnaire and the neuropsychologic battery were included for analysis in this study. A total of 826 individuals (mean [SD] age, 76.3 [11.8] years; 468 women [56.7%]; 217 apolipoprotein E ε4 allele carriers [26.3%]) had complete demographic information and were included in the study. Analysis using a Cox proportional hazard regression model (concordance, 0.76) showed that status as a short sleeper (hazard ratio, 3.67; 95% CI, 1.59-8.50) and higher sleep variability (hazard ratio, 3.06; 95% CI, 1.14-5.49) were significantly associated with the incidence of cognitive impairment.Conclusions and relevanceIn this community-based longitudinal study of the association between sleep patterns and cognitive performance, the short sleep phenotype was significantly associated with impaired cognitive performance. Furthermore, high sleep variability in longitudinal sleep duration was significantly associated with the incidence of cognitive impairment, highlighting the possibility that instability in sleep duration over long periods of time may impact cognitive decline in older adults.

Project description:BackgroundNo studies have assessed the association between daytime napping and obesity in China. The study aimed to examine the association between daytime napping and obesity among Chinese middle-aged and older adults, and to evaluate the difference between the aforementioned association in men and women.MethodsOverall, 14 685 participants aged 45 years and older were included by using data from China Health and Retirement Longitudinal Study (CHARLS) in 2015. A multivariable logistic regression model was used to investigate the relationship between daytime napping and obesity after adjusting for potential confounders. Stratified analyses were performed to examine the association differences by sex. Besides, the Cochran-Armitage test for trend was used to detect if there was a significant dose-response relationship between daytime napping and obesity.ResultsThe mean age of participants was 60.32, and the mean daytime napping duration was 38.97 minutes. In the sample, compared with no daytime napping group, the risks of being obese were higher in both moderate daytime napping group (1-60 minute/d) (odds ratio OR = 1.27, 95% confidence interval (CI) = 1.13-1.44) and extend long daytime napping group (>60 minute/d) (OR = 1.34, 95% CI = 1.15-1.56). In sex stratification, these significant correlations only existed in women but not in men. Compared with no daytime napping, women who napped 1-60 minute/d and over 60 minute/d were more likely to be obese (OR = 1.37, 95% CI = 1.18-1.59 and OR = 1.49, 95% CI = 1.23-1.81, respectively). Besides, the Cochran-Armitage trend test revealed that the prevalence rate of obesity increased as the daytime napping duration increased (P < 0.001).ConclusionsThe study established the relationship between daytime napping and obesity in a general Chinese population. The association, however, was only detected among women. Furthermore, there was a dose-response relationship between daytime napping and obesity among women. Future studies may verify this association by using a longitudinal design and focus on the mechanisms behind such association.

Project description:BackgroundSleep efficiency is often used as a measure of sleep quality. Getting sufficiently high-quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well-established.ObjectiveWe aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests.MethodsWe included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes.ResultsA total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, β=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, β=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, β=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, β=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, β=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, β=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, β=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, β=-.65, 95% CI -1.39 to 0.08) scores.ConclusionsTargeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.

Project description:ObjectivesTo investigate the association between depressive symptoms and several medical morbidities, and their combination, in a large older population.DesignCross-sectional study of baseline data from the ASPirin in Reducing Events in the Elderly (ASPREE) trial.SettingMulticentric study conducted in Australia and the United States.ParticipantsA total of 19,110 older adults (mean age = 75 years [standard deviation = ±4.5]).MeasurementsDepressive symptoms were measured using the Center for Epidemiological Studies Depression (CES-D 10) scale. Medical morbidities were defined according to condition-specific methods. Logistic regression was used to calculate odds ratios (ORs) with 95% confidence intervals (CIs) to test associations before and after accounting for possible confounders.ResultsDepressive symptoms were significantly associated with obesity (OR = 1.19; 95% CI = 1.07-1.32), diabetes (OR = 1.22; 95% CI = 1.05-1.42), gastroesophageal reflux disease (GERD) (OR = 1.41; 95% CI = 1.28-1.57), metabolic syndrome (OR = 1.16; 95% CI = 1.03-1.29), osteoarthritis (OR = 1.41; 95% CI = 1.27-1.57), respiratory conditions (OR = 1.25; 95% CI = 1.10-1.42), history of cancer (OR = 1.19; 95% CI = 1.05-1.34), Parkinson's disease (OR = 2.56; 95% CI = 1.83-3.56), polypharmacy (OR = 1.60; 95% CI = 1.44-1.79), and multimorbidity (OR = 1.29; 95% CI = 1.12-1.49). No significant association was observed between depressive symptoms and hypertension, chronic kidney disease, dyslipidemia, and gout (P > .05). A significant dose-response relationship was evident between the number of medical comorbidities and the prevalence of depression (OR = 1.18; 95% CI = 1.13-1.22).ConclusionLate-life depressive symptoms are significantly associated with several medical morbidities, and there appears to be a cumulative effect of the number of somatic diseases on the prevalence of depression. These findings augment the evidence for a complex relationship between mental and physical health in an otherwise healthy older population and might guide clinicians toward early recognition of high-risk individuals. J Am Geriatr Soc 68:1834-1841, 2020.

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The association between sleep patterns and obesity in older adults.

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The association between sleep patterns and obesity in older adults.

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