Evidence map›Paper›PMID 35034128›Full record

Observational studySleep2022

Associations of sleep and circadian phenotypes with COVID-19 susceptibility and hospitalization: an observational cohort study based on the UK Biobank and a two-sample Mendelian randomization study.

Zheran Liu, Yaxin Luo, Yonglin Su, Zhigong Wei, Ruidan Li, Ling He, Lianlian Yang, Yiyan Pei, Jianjun Ren, Xingchen Peng and 1 more

Open access · bronzeAbstract readObservational Study
In one paragraph

Observational study in Sleep, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 15 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Zheran LiuDepartment of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan,China.
Yaxin LuoDepartment of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan,China.
Yonglin SuWest China Hospital, Sichuan University, Chengdu, Sichuan,China.
Zhigong Wei
Ruidan LiDepartment of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan,China.
Ling HeDepartment of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan,China.
Lianlian YangDepartment of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan,China.
Yiyan PeiDepartment of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan,China.
Jianjun RenDepartment of Otolaryngology-Head and Neck Surgery, West China Biomedical Big Data Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China.
Xingchen PengDepartment of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan,China.
Xiaolin HuWest China School of Nursing, West China Hospital, Sichuan University, Chengdu, Sichuan,China.
Sichuan University · CNWest China Medical Center of Sichuan University · CNWest China Hospital of Sichuan University · CN

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

STUDY

objectivesSleep and circadian phenotypes are associated with several diseases. The present study aimed to investigate whether sleep and circadian phenotypes were causally linked with coronavirus disease 2019 (COVID-19)-related outcomes.

methodsHabitual sleep duration, insomnia, excessive daytime sleepiness, daytime napping, and chronotype were selected as exposures. Key outcomes included positivity and hospitalization for COVID-19. In the observation cohort study, multivariable risk ratios (RRs) and their 95% confidence intervals (CIs) were calculated. Two-sample Mendelian randomization (MR) analyses were conducted to estimate the causal effects of the significant findings in the observation analyses. Odds ratios (ORs) and the corresponding 95% CIs were calculated and compared using the inverse variance weighting, weighted median, and MR-Egger methods.

resultsIn the UK Biobank cohort study, both often excessive daytime sleepiness and sometimes daytime napping were associated with hospitalized COVID-19 (excessive daytime sleepiness [often vs. never]: RR = 1.24, 95% CI = 1.02-1.5; daytime napping [sometimes vs. never]: RR = 1.12, 95% CI = 1.02-1.22). In addition, sometimes daytime napping was also associated with an increased risk of COVID-19 susceptibility (sometimes vs. never: RR = 1.04, 95% CI = 1.01-1.28). In the MR analyses, excessive daytime sleepiness was found to increase the risk of hospitalized COVID-19 (MR IVW method: OR = 4.53, 95% CI = 1.04-19.82), whereas little evidence supported a causal link between daytime napping and COVID-19 outcomes.

conclusionsObservational and genetic evidence supports a potential causal link between excessive daytime sleepiness and an increased risk of COVID-19 hospitalization, suggesting that interventions targeting excessive daytime sleepiness symptoms might decrease severe COVID-19 rate.

Indexed as

COVID-19Disorders of Excessive SomnolenceBiological Specimen BanksCohort StudiesGenome-Wide Association StudyHospitalizationHumansMendelian Randomization AnalysisPhenotypeSleepUnited Kingdomcohort studyCOVID-19Mendelian randomizationsleep

Identifiers

PMID35034128
PMCPMC8807236
OpenAlexW4205686653

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.