Effect of Excessive Daytime Sleepiness and Long Sleep Duration on All Cause Dementia: A Systematic Review and Meta-analysis
Abstract
Abstract Background Excessive daytime sleepiness (EDS) and long sleep duration are common in older adults and are related to dementia pathology. This study aims to assess the effect of EDS and long sleep duration on all-cause dementia and cognitive decline. Methods We identified longitudinal studies assessing the relationship between EDS and/or long sleep duration on cognitive decline/dementia published up to March 24, 2024 from MEDLINE, Embase, PsyINFO, the Cochrane Central Register of Controlled Trials, and PubMed. The inverse-variance-weighted average method and Bayesian multilevel regression models were used to test the effect of EDS and long sleep duration on cognitive decline and dementia risk. Results Fifteen studies with 65 501 participants were recruited. Excessive daytime sleepiness was associated with increased risk of cognitive decline and all-cause dementia (risk ratio 1.26, 95% confidence interval [CI]: 1.13–1.41, and risk ratio 1.68, 95% CI: 1.07–2.66, respectively). Long sleep duration increased the risk of all-cause dementia by 29% (95% hazard ratio CI: 0.94–1.77, Pr [hazard ratio > 1] = 0.94); and cognitive decline by 13% (95% risk ratio, CI: 0.92–1.4). Dementia-free time at age 85 was 0.13 years shorter among long sleepers (Pr (∆ Restricted Mean Dementia-Free Time < 0) = 0.98). Conclusions Both EDS and long sleep duration were associated with increased risk of all-cause dementia. However, it remains unclear whether these are independent risk factors or…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 65 participants or records.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.