| Title: | Polygenic score for sleep duration in relation to the risk of Alzheimer's disease: results from the UK biobank |
| Journal: | Frontiers in Dementia |
| Published: | 27 Aug 2026 |
| DOI: | https://doi.org/10.3389/frdem.2026.1843867 |
| URL: | http://dx.doi.org/10.3389/frdem.2026.1843867 |
Publication 20880
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Abstract
Studies have suggested that sleep duration may be associated with Alzheimer's disease risk; however, findings based on self-reported sleep duration are likely to be influenced by reverse causation and residual confounding bias. We derived weights for genetic variants associated with wearable-derived sleep duration using the LDpred2-auto method in 77,770 white British participants from the UK Biobank, following the generation of new genome-wide association summary statistics. We then used these weights to generate polygenic scores (PGSs) for the remaining 264,746 white British participants for the association analysis, independent of the sample used to develop PGS weights. We assessed the association of fifths between genetically predicted sleep duration and the risk of Alzheimer's disease (1,451 cases/264,746 individuals over a median 12.5 years of follow-up). The PGS explained approximately 2% of the variation in device-measured sleep duration. Compared with individuals in the middle fifth of PGSs, those in the highest fifth (indicating approximately 15 min/day longer sleep) had a lower risk of Alzheimer's disease (hazard ratio (HR) = 0.79[95%CI, 0.67-0.94]). Our results indicate that genetic predisposition to relatively long sleep duration is associated with a lower Alzheimer's disease risk.</p>
8 Authors
- Angel T. Y. Wong
- Sarah Floud
- Gillian K. Reeves
- Michael V. Holmes
- Ruth C. Travis
- Cornelia M. van Duijn
- Aiden Doherty
- Karl Smith-Byrne
1 Application
| Application ID | Title |
|---|---|
| 59070 | Statistical machine learning of wearable sensor data to predict disease outcomes |
Enabling scientific discoveries that improve human health