scholarly journals 699 Sleep Health Traits and COVID-19: Mortality Risk from the UK Biobank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A273-A273
Author(s):  
Xi Zheng ◽  
Ma Cherrysse Ulsa ◽  
Peng Li ◽  
Lei Gao ◽  
Kun Hu
Keyword(s):  
Risk Factors ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Sex Education ◽  
Mortality Risk ◽  
Self Report ◽  
Severe Illness ◽  
Uk Biobank ◽  
Sleep Health ◽  
The Uk

Abstract Introduction While there is emerging evidence for acute sleep disruption in the aftermath of coronavirus disease 2019 (COVID-19), it is unknown whether sleep traits contribute to mortality risk. In this study, we tested whether earlier-life sleep duration, chronotype, insomnia, napping or sleep apnea were associated with increased 30-day COVID-19 mortality. Methods We included 34,711 participants from the UK Biobank, who presented for COVID-19 testing between March and October 2020 (mean age at diagnosis: 69.4±8.3; range 50.2–84.6). Self-reported sleep duration (less than 6h/6-9h/more than 9h), chronotype (“morning”/”intermediate”/”evening”), daytime dozing (often/rarely), insomnia (often/rarely), napping (often/rarely) and presence of sleep apnea (ICD-10 or self-report) were obtained between 2006 and 2010. Multivariate logistic regression models were used to adjust for age, sex, education, socioeconomic status, and relevant risk factors (BMI, hypertension, diabetes, respiratory diseases, smoking, and alcohol). Results The mean time between sleep measures and COVID-19 testing was 11.6±0.9 years. Overall, 5,066 (14.6%) were positive. In those who were positive, 355 (7.0%) died within 30 days (median = 8) after diagnosis. Long sleepers (>9h vs. 6-9h) [20/103 (19.4%) vs. 300/4,573 (6.6%); OR 2.09, 95% 1.19–3.64, p=0.009), often daytime dozers (OR 1.68, 95% 1.04–2.72, p=0.03), and nappers (OR 1.52, 95% 1.04–2.23, p=0.03) were at greater odds of mortality. Prior diagnosis of sleep apnea also saw a two-fold increased odds (OR 2.07, 95% CI: 1.25–3.44 p=0.005). No associations were seen for short sleepers, chronotype or insomnia with COVID-19 mortality. Conclusion Data across all current waves of infection show that prior sleep traits/disturbances, in particular long sleep duration, daytime dozing, napping and sleep apnea, are associated with increased 30-day mortality after COVID-19, independent of health-related risk factors. While sleep health traits may reflect unmeasured poor health, further work is warranted to examine the exact underlying mechanisms, and to test whether sleep health optimization offers resilience to severe illness from COVID-19. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

scholarly journals 1159 Sleep Traits And Incident Delirium During A Decade Of Follow-up In 173,000 Participants

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A442-A442
Author(s):  
L Gao ◽  
P Li ◽  
L Cui ◽  
O Johnson-Akeju ◽  
K Hu
Keyword(s):  
Sleep Apnea ◽  
Sleep Duration ◽  
Sleep Disturbances ◽  
Neurological Diseases ◽  
Self Report ◽  
Sleep Health ◽  
Logistic Regression Models ◽  
Increased Risk ◽  
Icd 10

Abstract Introduction Delirium is an acute decline in attention and cognition that is with associated long-term cognitive dysfunction in elderly patients. Accumulating evidence points to strong associations between sleep health and disorders of the brain. We tested whether baseline sleep duration, chronotype, daytime dozing, insomnia or sleep apnea predict incident delirium during hospitalization. Methods We studied participants from the UK Biobank who have been followed for up to 10 years until 2017. We included 173,221 participants (mean age 60±5; range 50-71 at baseline) who had at least one episode of hospitalization/surgery and were free from prior episodes of delirium. Delirium diagnosis, hospitalization and surgical events were derived using ICD-10 coding. Multivariate logistic regression models were performed to examine the associations of self-reported baseline sleep duration (<6hrs/6-9h/>9h), daytime dozing (often/rarely), insomnia (often/rarely) and presence of sleep apnea (ICD-10 and self-report) with incident delirium during follow-up. Models were adjusted for demographics, education, Townsend deprivation index, and major confounders (number of hospitalizations/surgical procedures, BMI, diabetes, major cardiovascular diseases and risk factors, major neurological diseases, major respiratory diseases, cancer, alcohol, depression/anxiety, sedatives/sleep aides, antipsychotics, steroids and opioids). Results In total, 1,023 (5.7 per 1,000 subjects) developed delirium. A prior diagnosis of sleep apnea (n=1,294) saw almost a two-fold increased odds (OR 1.96, 95% CI: 1.30-2.30 p=0.001) while those who often had daytime dozing were also at increased risk (OR 1.35, 95% CI: 1.02-1.80, p=0.025). Both these effects were independent of each other. No independent effects on incident delirium were observed from sleep duration, insomnia, or chronotype. Conclusion Certain sleep disturbances, in particular sleep apnea and daytime dozing, are independently associated with an increased risk for developing delirium. Further work is warranted to examine underlying mechanisms and to test whether optimizing sleep health can reduce the risk of developing delirium. Support This work was supported by NIH grants T32GM007592, RF1AG064312, and RF1AG059867.

547 Earlier-life sleep patterns and risk for delirium in elderly hospitalized patients from a 14-year longitudinal cohort

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A215-A216
Author(s):  
Ma Cherrysse Ulsa ◽  
Xi Zheng ◽  
Peng Li ◽  
Kun Hu ◽  
Lei Gao
Keyword(s):  
Sleep Apnea ◽  
Sleep Duration ◽  
Sex Education ◽  
Sleep Disturbances ◽  
Sleep Patterns ◽  
Logistic Regression Models ◽  
Increased Risk ◽  
Icd 10 ◽  
The Uk

Abstract Introduction Delirium is an acute decline in attention and cognition that is associated with cognitive dysfunction in elderly patients. While accumulating evidence points to associations between sleep disturbances and neurocognitive disorders, the temporal relationship between sleep patterns and delirium remains unclear. We tested whether earlier-life sleep duration, daytime dozing, insomnia, and sleep apnea predict incident delirium during hospitalization. Methods We studied 315,989 participants (mean age 58.3±7.9; range 37.4–73.7) from the UK Biobank with up to 14 years follow-up, and at least one hospitalization episode. Delirium diagnosis was derived using ICD-10 coding from hospitalization records. Multivariate logistic regression models examined the associations of self-reported baseline sleep duration (less than 6h/6-9h/more than 9h), daytime dozing (often/rarely), insomnia (often/rarely), and presence of prior sleep apnea (ICD-10), with incident delirium. Models were adjusted for age, sex, education, Townsend deprivation index, and major confounders (including number of hospitalizations during follow-up, BMI, neurological/cardiovascular/respiratory diseases, depression/anxiety, chronotype, and sedatives). Results 4,025 developed delirium (12.7/1,000). There was a U-shaped association between sleep duration and delirium, where short [17.3/1,000; OR 1.18, 95% CI: 1.05–1.33, p=0.006] and long (28.8/1,000; OR 1.49, 95% CI: 1.30–1.70, p<0.001) sleepers had elevated risk compared to regular 6-9h sleepers. Often daytime dozing (25.3/1,000; OR 1.38, 95% CI: 1.20–1.58, p<0.001) and sleep apnea (21.7/1,000; OR 1.21, 95% CI: 1.03–1.42 p=0.02) also had increased the risk for delirium, but the latter was attenuated by the inclusion of BMI and hypertension. However, we did observe further risk when two or more of the above traits were present (OR 1.59, 95% CI: 1.29–1.95 p<0.001). No effects on incident delirium were observed from insomnia. Conclusion Earlier-life sleep patterns, in particular longer sleep and daytime dozing, are associated with an increased risk for delirium. Sleep patterns may reflect unmeasured health status; further work is warranted to confirm the associations using objective sleep/circadian measures, examine underlying mechanisms, and test whether optimizing sleep patterns can reduce the risk of developing delirium. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

scholarly journals Correlations in sleeping patterns and circadian preference between spouses

2022 ◽  
Author(s):  
Rebecca C Richmond ◽  
Laurence J Howe ◽  
Karl Heilbron ◽  
Samuel Jones ◽  
Junxi Liu ◽  
...  
Keyword(s):  
Sleep Duration ◽  
Inverse Correlation ◽  
Self Report ◽  
Phenotypic Correlation ◽  
Accelerometer Data ◽  
Diurnal Activity ◽  
Uk Biobank ◽  
Positive Effects ◽  
Diurnal Preference ◽  
The Uk

Spouses may affect each other's sleeping behaviour. In 47,420 spouse-pairs from the UK Biobank, we found a weak positive phenotypic correlation between spouses for self-reported sleep duration (r=0.11; 95% CI=0.10, 0.12) and a weak inverse correlation for chronotype (diurnal preference) (r=-0.11; -0.12, -0.10), which replicated in up to 127,035 23andMe spouse-pairs. Using accelerometer data on 3,454 UK Biobank spouse-pairs, the correlation for derived sleep duration was similar to self-report (r=0.12; 0.09, 0.15). Timing of diurnal activity was positively correlated (r=0.24; 0.21, 0.27) in contrast to the inverse correlation for chronotype. In Mendelian randomization analysis, positive effects of sleep duration (mean difference=0.13; 0.04, 0.23 SD per SD) and diurnal activity (0.49; 0.03, 0.94) were observed, as were inverse effects of chronotype (-0.15; -0.26, -0.04) and snoring (-0.15; -0.27, -0.04). Findings support the notion that an individual's sleep may impact that of their partner, with implications for sleep health.

scholarly journals Sex differences in the association between major cardiovascular risk factors in midlife and dementia: a cohort study using data from the UK Biobank

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jessica Gong ◽  
Katie Harris ◽  
Sanne A. E. Peters ◽  
Mark Woodward
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Sex Differences ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Sex Difference ◽  
Incidence Rates ◽  
Cox Proportional Hazards ◽  
Uk Biobank ◽  
The Uk

Abstract Background Sex differences in major cardiovascular risk factors for incident (fatal or non-fatal) all-cause dementia were assessed in the UK Biobank. The effects of these risk factors on all-cause dementia were explored by age and socioeconomic status (SES). Methods Cox proportional hazards models were used to estimate hazard ratios (HRs) and women-to-men ratio of HRs (RHR) with 95% confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP), smoking, diabetes, adiposity, stroke, SES and lipids with dementia. Poisson regression was used to estimate the sex-specific incidence rate of dementia for these risk factors. Results 502,226 individuals in midlife (54.4% women, mean age 56.5 years) with no prevalent dementia were included in the analyses. Over 11.8 years (median), 4068 participants (45.9% women) developed dementia. The crude incidence rates were 5.88 [95% CI 5.62–6.16] for women and 8.42 [8.07–8.78] for men, per 10,000 person-years. Sex was associated with the risk of dementia, where the risk was lower in women than men (HR = 0.83 [0.77–0.89]). Current smoking, diabetes, high adiposity, prior stroke and low SES were associated with a greater risk of dementia, similarly in women and men. The relationship between blood pressure (BP) and dementia was U-shaped in men but had a dose-response relationship in women: the HR for SBP per 20 mmHg was 1.08 [1.02–1.13] in women and 0.98 [0.93–1.03] in men. This sex difference was not affected by the use of antihypertensive medication at baseline. The sex difference in the effect of raised BP was consistent for dementia subtypes (vascular dementia and Alzheimer’s disease). Conclusions Several mid-life cardiovascular risk factors were associated with dementia similarly in women and men, but not raised BP. Future bespoke BP-lowering trials are necessary to understand its role in restricting cognitive decline and to clarify any sex difference.

Variation of cardiac magnetic resonance radiomics features by age and sex in healthy participants from the UK Biobank

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
Z Raisi-Estabragh ◽  
A Jaggi ◽  
N Aung ◽  
S Neubauer ◽  
S Piechnik ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiac Magnetic Resonance ◽  
Age Groups ◽  
Texture Features ◽  
Funding Source ◽  
Uk Biobank ◽  
Men And Women ◽  
The Uk ◽  
Age And Sex ◽  
Apparently Healthy

Abstract Introduction Cardiac magnetic resonance (CMR) radiomics use voxel-level data to derive quantitative indices of myocardial tissue texture, which may provide complementary risk information to traditional CMR measures. Purpose In this first stage of our work, establishing the performance characteristics of CMR radiomics in relation to disease outcomes, we aimed to elucidate differences in radiomic features by sex and age in apparently healthy adults. Methods We defined a healthy cohort from the first 5,065 individuals completing the UK Biobank Imaging Enhancement, limiting to white Caucasian ethnicity, and excluding those with major co-morbidities, or cardiovascular risk factors/symptoms. We created evenly distributed age groups: 45–54 years, 55–64 years, 65–74 years. Radiomics features were extracted from left ventricle segmentations, with normalisation to body surface area. We compared mean values of individual features between the sexes, stratified by age and separately between the oldest and youngest age groups for each sex. Results We studied 657 (309 men, 358 women) healthy individuals. There were significant differences between radiomics features of men and women. Different features appeared more important at different age groups. For instance, in the youngest age group “end-systolic coarseness” showed greatest difference between men and women, whilst “end-diastolic run percentage” and “end-diastolic high grey level emphasis” showed most variation in the oldest and middle age groups. In the oldest age groups, differences between men and women were most predominant in the texture features, whilst in the younger groups a mixture of shape and texture differences were observed. We demonstrate significant variation between radiomics features by age, these differences are exclusively in texture features with different features implicated in men and women (“end-diastolic mean intensity” in women, “end-systolic sum entropy in men”). Conclusions There are significant age and sex differences in CMR radiomics features of apparently healthy adults, demonstrating alterations in myocardial architecture not appreciated by conventional indices. In younger ages, shape and texture differences are observed, whilst in older ages texture differences dominate. Furthermore, texture features are the most different features between the youngest and oldest hearts. We provide proof-of-concept data indicating CMR radiomics has discriminatory value with regard to two characteristics strongly linked to cardiovascular outcomes. We will next elucidate relationships between CMR radiomics, cardiac risk factors, and clinical outcomes, establishing predictive value incremental to existing measures. Funding Acknowledgement Type of funding source: Other. Main funding source(s): European Union's Horizon 2020 research and innovation programme (825903),British Heart Foundation Clinical Research Training Fellowship (FS/17/81/33318)

Abstract P195: Lack of Association between Sleep Duration and ECG Arrhythmia Risk Measures: Chicago Area Sleep Study

Circulation ◽  
2013 ◽  
Vol 127 (suppl_12) ◽  
Author(s):  
Jason Ng ◽  
Phyllis C Zee ◽  
Jeffrey J Goldberger ◽  
Kristen L Knutson ◽  
Kiang Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Low Risk ◽  
Qtc Interval ◽  
Sleep Study ◽  
Chicago Area ◽  
Obstructive Sleep ◽  
Pr Interval

Introduction Sleep duration is significantly associated with cardiovascular disease risk factors such as hypertension, diabetes, and obesity in adults at low risk for obstructive sleep apnea. Although it is known that apnea increases the risk for sudden cardiac death, it is not known whether adults with short sleep duration independent of apnea have a higher risk for cardiac arrhythmias Hypothesis We tested the hypothesis that sleep duration in adults at low risk for obstructive sleep apnea would be associated with ECG measures that are known risk factors for ventricular arrhythmias. Methods The Chicago Area Sleep Study recruited 610 participants via commercially available telephone listings. Participants were screened using in-home apnea detection equipment (ApneaLinkTM) for one night to exclude subjects with apnea/hypopnea index ≥ 15. Participants wore wrist actigraphs for 7 days to objectively determine sleep duration. A 10-minute 12-lead ECG was recorded for each subject. Standard measures of heart rate, PR interval, and QTc interval were obtained along with markers of ventricular repolarization, Tpeak to Tend interval (Tpe) and spatial QRS-T angle. Signal-averaged ECG analysis was performed to measure filtered QRS duration (fQRSd), RMS voltage of terminal 40 ms (RMS), and duration of terminal QRS signals <40μV (LAS). Participants with atrial fibrillation, >20% ectopic beats and those using antihypertensive and sleep medications were excluded from analysis. The effect of sleep duration on the ECG parameters was estimated using a multiple linear regression model adjusting for demographics (sex, age, and race) and cardiovascular risk factors (BMI, hypertension, coronary heart disease, and diabetes). Results ECGs from a total of 504 participants (200 male, 48±8 years old) were analyzed. Mean sleep duration was 7±1 hrs, heart rate was 64±9 bpm, PR interval was 165±18 ms, and QTc interval was 424±23 ms. Mean Tpe interval was 83±14 ms and spatial QRS-T angle was 29±26 deg. The signal-averaged ECG measures of fQRSd, RMS, and LAS had mean values of 78±12 ms, 58±34 μV, and 24±9 ms, respectively. In an unadjusted model, there was a borderline association between sleep duration and QTc (β=0.004 ms/hr, SE=0.0023, p=0.08). However, that association was no longer significant following adjustment with demographics and cardiovascular risk factors. No other ECG measures were associated with sleep duration. Conclusions In a population at low risk of obstructive sleep apnea, ECG-based measures of cardiovascular risks were not associated with sleep duration. Previously reported associations between short sleep and cardiovascular events may not be arrhythmic in origin.

scholarly journals Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank

PLoS Medicine ◽  
2021 ◽  
Vol 18 (10) ◽  
pp. e1003782
Author(s):  
Michael Wainberg ◽  
Samuel E. Jones ◽  
Lindsay Melhuish Beaupre ◽  
Sean L. Hill ◽  
Daniel Felsky ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Sleep Duration ◽  
Sleep Efficiency ◽  
Risk Scores ◽  
Psychiatric Diagnoses ◽  
Uk Biobank ◽  
Major Depressive ◽  
Cross Sectional ◽  
Polygenic Risk ◽  
The Uk

Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

scholarly journals Vaping and subsequent comorbidities potentially associated with increased mortality and more severe illness in COVID-19: a narrative review

2021 ◽  
Keyword(s):  
Risk Factors ◽  
Cigarette Smoking ◽  
Health Risks ◽  
Older Patients ◽  
Significant Risk ◽  
Self Report ◽  
Severe Illness ◽  
Protective Effects ◽  
Cigarette Use

Introduction: COVID-19 (or COVID) is a highly virulent viral disease which more frequently presents severe infection in specific populations, such as the elderly, patients with hypertension, patients with respiratory disease, and patients who smoke. The effects vaping (i.e., an electronic cigarette or JUUL device) has on COVID progression remains unclear, because there is an information paucity correlating e-cigarette use and COVID. This review sought to identify links between vape use and COVID severity via literature review. Additionally, because there is more widespread information about cigarette smoking than about vaping, this review sought to illustrate commonalities between smoking and vaping. If smoking and vaping are deemed near-identical practices, then it is possible the effects of smoking on human health and on COVID disease could be comparable in vaping. Methods: Several searches were performed on PubMed with MeSH headings and JSTOR between 17 December 2020 and 22 December 2020. Search results were excluded if they were not trials or controlled clinical trials, if the articles were not about COVID, if the articles were about smoking behaviors or habits, or if the articles were not related to vaping or smoking. Key findings were summarized and tabled based on relevance, substantiability, and applicability to COVID. Results: Multiple sources viewed smoking and vaping as equal risk factors for COVID disease, whereas other sources viewed the two as unique risk factors. Because of this controversy, it is challenging to view the two practices as similar enough to pose equivalent risks for COVID. Both practices pose significant health risks to its users, but these health risks are unique to each practice. Discussion: There are several limitations which exacerbate ambiguity—(1) it is unclear how harmful smoking is for COVID patients, because several publications found smoking may have protective effects; (2) few older patients vape, but yet most severe COVID cases occur in older populations; (3) older patients and impoverished patients show a statistically significant risk for severe COVID disease independent of other factors; (4) vaping is a relatively new practice, and there are few patients who self-report long-term e-cigarette use or long-term adverse effects as a result thereof. Conclusion: Although vaping may present serious health risks, clinically, it is uncertain how significantly vaping affects COVID disease, especially when compared against cigarette smoking. More research is needed on both the effects of vaping on COVID and the likeness of vaping versus smoking.

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