| Title: | Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank |
| Journal: | Biomedicines |
| Published: | 31 Aug 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39335481/ |
| DOI: | https://doi.org/10.3390/biomedicines12091967 |
Publication 13048
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Abstract
This study investigates the relationship between modifiable risk factors and dementia subtypes using data from 460,799 participants in the UK Biobank. Utilizing univariate Cox proportional hazards regression models, we examined the associations between 83 modifiable risk factors and the risks of all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD). Composite scores for different domains were generated by aggregating risk factors associated with ACD, AD, and VD, respectively, and their joint associations were assessed in multivariable Cox models. Additionally, population attributable fractions (PAF) were utilized to estimate the potential impact of eliminating adverse characteristics of the risk domains. Our findings revealed that an unfavorable medical history significantly increased the risk of ACD, AD, and VD (hazard ratios (HR) = 1.88, 95% confidence interval (95% CI): 1.74-2.03, p < 0.001; HR = 1.80, 95% CI: 1.54-2.10, p < 0.001; HR = 2.39, 95% CI: 2.10-2.71, p < 0.001, respectively). Blood markers (PAF = 12.1%; 17.4%) emerged as the most important risk domain for preventing ACD and VD, while psychiatric factors (PAF = 18.3%) were the most important for preventing AD. This study underscores the potential for preventing dementia and its subtypes through targeted interventions for modifiable risk factors. The distinct insights provided by HR and PAF emphasize the importance of considering both the strength of the associations and the population-level impact of dementia prevention strategies. Our research provides valuable guidance for developing effective public health interventions aimed at reducing the burden of dementia, representing a significant advancement in the field.</p>
6 Authors
- Xiangge Ma
- Hongjian Gao
- Yutong Wu
- Xinyu Zhu
- Shuicai Wu
- Lan Lin
1 Application
| Application ID | Title |
|---|---|
| 68382 | Effects of environmental and behavioural factors on the development of aging brain via multi-modal imaging |
Enabling scientific discoveries that improve human health