Sleep duration, genetic susceptibility, and Alzheimer's disease: a longitudinal UK Biobank-based study

Yuan, Shiqi; Ma, Wen; Yang, Rui; Xu, Fengshuo; Han, Didi; Peng, MIn; Xu, Anding; Lyu, Jun

doi:10.1186/s12877-022-03298-8

Cited by 22 publications

(12 citation statements)

References 51 publications

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“…Our findings provide a new line of evidence in favour of the potential role of sleep duration in Alzheimer's disease risk. These findings contrast with multiple previously published MR studies that did not find evidence for an association between sleep duration and Alzheimer's disease [6][7][8]31,32]. However, statistical power is notably limited in MR studies given the low variance explained by the genetic instruments used to predict sleep duration.…”

Section: Discussioncontrasting

confidence: 93%

“…Thus far, GWAS have identified few single-nucleotide polymorphisms (SNPs) for device-derived sleep duration [4,5], which explain limited trait variance (0.33%) [4]. Given GWAS sample sizes (n~91,000) and the limited variation explained by previously identified significant SNPs, investigation of sleep duration and dementia risk using Mendelian randomisation (MR) may be underpowered, as reflected by the wide confidence intervals around the estimates in previous studies [6][7][8]. We have previously reported that up to 18% of the phenotypic variance could be explained by common SNPs (P<5-e-3) [4], so we hypothesise that a PGS based on this wider range of genetic variants may increase power to better investigate the role of sleep duration in dementia risk.…”

Section: Introductionmentioning

confidence: 99%

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Polygenic score for sleep duration in relation to risk of Alzheimer’s disease: results from the UK Biobank

Wong

Floud

Reeves

et al. 2022

Preprint

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INTRODUCTION: Studies have suggested sleep duration may be associated with Alzheimer's disease risk, but findings based on self-reported sleep duration are likely to be influenced by reverse causation and residual confounding bias. METHODS: A polygenic score (PGS) for device-measured sleep duration was constructed using LDpred2-auto in 77,770 white British UK Biobank participants. We applied the PGS to 264,746 white British participants independent of the sample from which the PGS was developed. We assessed the association of fifths of genetically predicted sleep duration with Alzheimer's disease risk (1,451 cases/264,746 individuals over median 12.5y of follow-up). RESULTS: The PGS explained ~2% of variation in device-measured sleep duration. Compared to individuals in the middle fifth of PGS, those in the highest fifth (indicating ~15 mins/day longer sleep) had a lower risk of Alzheimer's disease (HR=0.79[95%CI,0.67-0.94]). DISCUSSION: Our results indicate that genetic predisposition to relatively long sleep duration is associated with a lower Alzheimer's disease risk.

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Section: Discussioncontrasting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Polygenic score for sleep duration in relation to risk of Alzheimer’s disease: results from the UK Biobank

Wong

Floud

Reeves

et al. 2022

Preprint

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“…We found a discrepancy between self-reported and PSG-measured sleep duration in those who developed NDDs, with shorter objective TST and longer reported sleep duration. Previous observational and Mendelian randomized studies showed an association between prolonged self-reported sleep duration and incident dementia 35, 36 . These and our findings emphasize the need for objective sleep efficiency and duration measures.…”

Section: Discussionmentioning

confidence: 91%

Sleep features and long-term incident neurodegeneration: a polysomnographic study

Ibrahim

Cesari

Heidbreder

et al. 2023

Preprint

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While a growing body of studies suggests a link between sleep disturbances and neurodegenerative diseases' (NDDs) development, prior studies have been hindered by small sample sizes, short follow-up times and a lack of objective sleep measures. In this cohort study, patients who underwent polysomnography (PSG) at the Innsbruck Sleep Disorders Unit from January 2004 to December 2007, aged ≥18 years, without NDDs at baseline or within five years post PSG, and with at least five years clinical follow-up were included. The main outcome measure was NDDs diagnosis at least five years after polysomnography, assessed until December 2021. Of 1454 patients assessed for eligibility, 999 (68.7%) met inclusion criteria (683 (68.3%) men; median age 54.9 (interquartile range, IQR 33.9-62.7) years. Seventy-five patients (7.5%) developed NDDs, 924 (92.5%) remained disease-free after 12.8 (IQR 9.9-14.6) years median follow-up. After adjusting for demographic, sleep, and clinical covariates, each percent decrease in sleep efficiency, N3 sleep, or REM sleep was associated with 1.9%, 6.5%, and 5.2% increased risk of incident NDDs (hazard ratio, HR, 1.019, CI:1.002-1.035; HR 1.065, CI:1.007-1.118; HR 1.052, CI:1.012-1.085,), respectively whereas one percent decrease in night-time wakefulness represented a 2.2% reduced risk (HR 0.978, CI:0.958-0.997). Random forest analysis identified wake, followed by N3 and REM sleep percentages, as the most important feature associated with NDDs development. Additionally, multiple sleep features combination offered more robust discrimination of incident NDDs compared to single sleep stages. These findings support contribution of sleep architecture changes to NDDs pathogenesis and provide insights into the temporal window during which these changes are detectable, pointing to sleep as early NDDs marker and potential target of neuroprotective strategies.

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“…There are many risk factors for the development of cognitive impairment in older adults; for example, excessive sleep duration increases the risk after adjusting for numerous relevant risk factors (Yuan et al, 2022a ), and those with a low BMI (< 23 kg/m 2 ) have a higher risk of developing dementia (Yuan et al, 2022b ). With the advent of an aging society, cognitive impairment occurrence will become more common (Afzal et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning for the prediction of cognitive impairment in older adults

Zeng

Yuan

et al. 2023

Front. Neurosci.

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ObjectiveThe purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm.MethodsThe complete data of 2,226 participants aged 60–80 years were extracted from the 2011–2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application.ResultsThe study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application.ConclusionsML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly.

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Sleep duration, genetic susceptibility, and Alzheimer's disease: a longitudinal UK Biobank-based study

Cited by 22 publications

References 51 publications

Polygenic score for sleep duration in relation to risk of Alzheimer’s disease: results from the UK Biobank

Polygenic score for sleep duration in relation to risk of Alzheimer’s disease: results from the UK Biobank

Sleep features and long-term incident neurodegeneration: a polysomnographic study

Machine learning for the prediction of cognitive impairment in older adults

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