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 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 Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour

2018 ◽  
Author(s):  
Samuel E. Jones ◽  
Vincent T. van Hees ◽  
Diego R. Mazzotti ◽  
Pedro Marques-Vidal ◽  
Séverine Sabia ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Sleep Duration ◽  
Genome Wide Association Study ◽  
Missense Variant ◽  
Causal Variant ◽  
Self Report ◽  
Accelerometer Data ◽  
Genetic Associations ◽  
A Genome ◽  
The Uk

ABSTRACTSleep is an essential human function but its regulation is poorly understood. Identifying genetic variants associated with quality, quantity and timing of sleep will provide biological insights into the regulation of sleep and potential links with disease. Using accelerometer data from 85,670 individuals in the UK Biobank, we performed a genome-wide association study of 8 accelerometer-derived sleep traits, 5 of which are not accessible through self-report alone. We identified 47 genetic associations across the sleep traits (P<5×10-8) and replicated our findings in 5,819 individuals from 3 independent studies. These included 26 novel associations for sleep quality and 10 for nocturnal sleep duration. The majority of newly identified variants were associated with a single sleep trait, except for variants previously associated with restless legs syndrome that were associated with multiple sleep traits. Of the new associated and replicated sleep duration loci, we were able to fine-map a missense variant (p.Tyr727Cys) in PDE11A, a dual-specificity 3’,5’-cyclic nucleotide phosphodiesterase expressed in the hippocampus, as the likely causal variant. As a group, sleep quality loci were enriched for serotonin processing genes and all sleep traits were enriched for cerebellar-expressed genes. These findings provide new biological insights into sleep characteristics.

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 Detecting retinal neurodegeneration in people with diabetes: Findings from the UK Biobank

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257836
Author(s):  
Roomasa Channa ◽  
Kyungmoo Lee ◽  
Kristen A. Staggers ◽  
Nitish Mehta ◽  
Sidra Zafar ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Glycated Hemoglobin ◽  
Retinal Thickness ◽  
Self Report ◽  
Inner Plexiform Layer ◽  
Uk Biobank ◽  
Fiber Layer ◽  
Cross Sectional ◽  
Retinal Neurodegeneration ◽  
The Uk

Importance Efforts are underway to incorporate retinal neurodegeneration in the diabetic retinopathy severity scale. However, there is no established measure to quantify diabetic retinal neurodegeneration (DRN). Objective We compared total retinal, macular retinal nerve fiber layer (mRNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness among participants with and without diabetes (DM) in a population-based cohort. Design/setting/participants Cross-sectional analysis, using the UK Biobank data resource. Separate general linear mixed models (GLMM) were created using DM and glycated hemoglobin as predictor variables for retinal thickness. Sub-analyses included comparing thickness measurements for patients with no/mild diabetic retinopathy (DR) and evaluating factors associated with retinal thickness in participants with and without diabetes. Factors found to be significantly associated with DM or thickness were included in a multiple GLMM. Exposure Diagnosis of DM was determined via self-report of diagnosis, medication use, DM-related complications or glycated hemoglobin level of ≥ 6.5%. Main outcomes and measures Total retinal, mRNFL and GC-IPL thickness. Results 74,422 participants (69,985 with no DM; 4,437 with DM) were included. Median age was 59 years, 46% were men and 92% were white. Participants with DM had lower total retinal thickness (-4.57 μm, 95% CI: -5.00, -4.14; p<0.001), GC-IPL thickness (-1.73 μm, 95% CI: -1.86, -1.59; p<0.001) and mRNFL thickness (-0.68 μm, 95% CI: -0.81, -0.54; p<0.001) compared to those without DM. After adjusting for co-variates, in the GLMM, total retinal thickness was 1.99 um lower (95% CI: -2.47, -1.50; p<0.001) and GC-IPL was 1.02 μm lower (95% CI: -1.18, -0.87; p<0.001) among those with DM compared to without. mRNFL was no longer significantly different (p = 0.369). GC-IPL remained significantly lower, after adjusting for co-variates, among those with DM compared to those without DM when including only participants with no/mild DR (-0.80 μm, 95% CI: -0.98, -0.62; p<0.001). Total retinal thickness decreased 0.40 μm (95% CI: -0.61, -0.20; p<0.001), mRNFL thickness increased 0.20 μm (95% CI: 0.14, 0.27; p<0.001) and GC-IPL decreased 0.26 μm (95% CI: -0.33, -0.20; p<0.001) per unit increase in A1c after adjusting for co-variates. Among participants with diabetes, age, DR grade, ethnicity, body mass index, glaucoma, spherical equivalent, and visual acuity were significantly associated with GC-IPL thickness. Conclusion GC-IPL was thinner among participants with DM, compared to without DM. This difference persisted after adjusting for confounding variables and when considering only those with no/mild DR. This confirms that GC-IPL thinning occurs early in DM and can serve as a useful marker of DRN.

scholarly journals Association ofDAT1genetic variants with habitual sleep duration in the UK Biobank

SLEEP ◽  
2018 ◽  
Vol 42 (1) ◽  
Author(s):  
Jessica A Rhodes ◽  
Jacqueline M Lane ◽  
Irma M Vlasac ◽  
Martin K Rutter ◽  
Charles A Czeisler ◽  
...  
Keyword(s):  
Sleep Duration ◽  
Uk Biobank ◽  
The Uk

scholarly journals Accuracy of Patient Self-Report of Stroke: A Systematic Review from the UK Biobank Stroke Outcomes Group

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0137538 ◽  
Author(s):  
Rebecca Woodfield ◽  
Cathie L. M. Sudlow ◽  
◽  
Keyword(s):  
Systematic Review ◽  
Self Report ◽  
Uk Biobank ◽  
Stroke Outcomes ◽  
The Uk

scholarly journals Mental health in the UK Biobank: a roadmap to self-report measures and neuroimaging correlates

2021 ◽  
Author(s):  
Rosie K Dutt ◽  
Kayla Hannon ◽  
Ty O Easley ◽  
Joseph Griffis ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Mental Health ◽  
Longitudinal Research ◽  
Self Report ◽  
Uk Biobank ◽  
State Dependent ◽  
Population Mental Health ◽  
Timing Effects ◽  
The Uk ◽  
Biomarker Research ◽  
Test Retest Reliability

The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was highly replicable. Conversely, effect sizes for individual IDPs were very small and contributions of individual IDPs to the multivariate result did not replicate. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.

P11.03 Sleep analysis of accelerometer data from High Grade Glioma patients in the BrainWear study

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii29-ii29
Author(s):  
M George ◽  
S Dadhania ◽  
M Williams
Keyword(s):  
High Grade Glioma ◽  
Poor Sleep ◽  
Common Symptom ◽  
Accelerometer Data ◽  
High Grade ◽  
Healthy Controls ◽  
Uk Biobank ◽  
Post Surgery ◽  
Objective Evidence ◽  
The Uk

Abstract BACKGROUND Sleep disturbance is a common symptom in patients with high grade glioma (HGG). Existing self-reported and uni-dimensional data from questionnaires are of limited value. The observational phase 2 trial, BrainWear (ISRCTN 34351424) provides the first objective analysis of sleep in HGG patients. MATERIAL AND METHODS Patients with HGG were asked to wear an AX3 Axivity tri-axial accelerometer throughout treatment. The study employed a wear-as-long-as-possible approach to accelerometry data collection, and we used age-sex matched controls from the UK Biobank as comparators. Baseline was established as a 7 day period of wear prior to surgery or at least 7 days post-surgery. The dataset for this analysis consists of 21 patients with data at baseline and 15 patients during chemo-radiation. Only 16 of the 21 HGG patients at baseline were included for initial comparisons with healthy controls due to age limitations of the UK Biobank cohort for matching. Raw accelerometer data was processed using the GGIR package, with non-imputation of missing data, exclusion of days with &lt;16 hours of wear time and removal of algorithm-identified problematic data. Mann-Whitney U-tests and unpaired T-tests were used to compare 7 sleep-related features between HGG patients and healthy controls at baseline, with choice of statistical test based on shapiro-wilk derived normality. Secondly, to assess changes in sleep in HGG patients across treatment period, K-means clustering of 5 sleep parameters, available longitudinally, was conducted to explore sleep behaviours at baseline (n = 21) and during chemo-radiation. RESULTS HGG patients (n = 16) exhibited greater daytime inactivity than healthy controls (n = 32) (p &lt; 0.0001, 2.2 vs 0.5 hrs) and more variation in their 24 hour activity rhythm from day to day (p &lt; 0.0001, 0.12 vs 0.18). We identified 5 sleep features which allowed us to cluster patients’ sleep behaviour, and most (62.5%) of HGG patients have a poor sleep profile. This sleep profile was characterised by an average of 5.4 hours of night-time sleep, 2.1 hours of daytime inactivity and disturbed sleep quality. However, evaluation of HGG patient sleep cluster designation at baseline and during chemoradiation, showed HGG patients with data at both timepoints (n = 9) demonstrate stability or improvement in sleep profile. CONCLUSION Patients with HGG have objective evidence of poor sleep compared to healthy matched controls. Further work will explore changes in sleep over time.

scholarly journals Using genetics to understand the causal influence of higher BMI on depression

2018 ◽  
Vol 48 (3) ◽  
pp. 834-848 ◽  
Author(s):  
Jessica Tyrrell ◽  
Anwar Mulugeta ◽  
Andrew R Wood ◽  
Ang Zhou ◽  
Robin N Beaumont ◽  
...  
Keyword(s):  
Risk Score ◽  
Genetic Risk ◽  
Mendelian Randomization ◽  
Genetic Risk Score ◽  
Self Report ◽  
Uk Biobank ◽  
Men And Women ◽  
Statistical Confidence ◽  
The Uk ◽  
Genetically Determined

Abstract Background Depression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women. Methods We performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to ‘uncouple’ the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways. Results Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence. Conclusions Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.

Sign in / Sign up

Export Citation Format

Share Document