| Title: | The interaction between circadian syndrome and genetic susceptibility in the risk of incident dementia: A longitudinal cohort study |
| Journal: | The Journal of Prevention of Alzheimer's Disease |
| Published: | 7 Feb 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39922757/ |
| DOI: | https://doi.org/10.1016/j.tjpad.2025.100089 |
Publication 14425
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Abstract
BACKGROUND: Despite growing interest in circadian disturbances as potential triggers for dementia, the specific impact of circadian syndrome (CircS) on dementia incidence remains poorly understood. Moreover, the role of genetic susceptibility modulating these effects remains to be explored.</p>
METHODS: Dementia-free participants from the UK Biobank cohort were included in the analysis. To evaluate the association between CircS and the incidence of dementia, as well as the modifying influence of genetic susceptibility on this relationship, Cox proportional hazards models were utilized.</p>
RESULTS: During a median follow-up period of 14.55 years, 3,965 incident dementia cases were documented. CircS was found to significantly increased the risk of incident dementia, with a hazard ratio (HR) of 1.401 (95 % confidence interval [CI]: 1.296, 1.516). Compared to a CircS score of ≤3, mild CircS (HR: 1.259, 95 % CI: 1.146-1.383), moderate CircS (HR: 1.667, 95 % CI: 1.461-1.903), and severe CircS (HR: 2.028, 95 % CI: 1.397-2.944) were all significantly associated with an elevated risk of dementia. There were significant multiplicative interactions between CircS and genetic susceptibility (Pinteraction<0.001). Participants with both a high polygenic risk score (PRS) and CircS had the highest risk of incident dementia (HR: 2.551, 95 % CI: 2.169, 3.001), compared to those with a low PRS and no CircS.</p>
CONCLUSIONS: CircS was associated with an increased risk of dementia, which might be aggravated by genetic susceptibility.</p>
7 Authors
- Linling Yu
- Wei Liu
- Chenqi Liao
- Na Shen
- Anding Liu
- Liming Cheng
- Xiong Wang
1 Application
| Application ID | Title |
|---|---|
| 106528 | Big Data Mining and Deep Learning to Improve Risk Prediction, Early diagnosis and Prognosis Evaluation of Chronic Diseases |
Enabling scientific discoveries that improve human health