Re-evaluating sleep difficulty as a risk factor for dementia in the UK Biobank cohort: addressing survival bias using a semi-competing risks approach

发布时间:2025-12-01浏览量:

论文摘要:

Sleep difficulty, a prevalent sleep disturbance in ageing populations, has shown paradoxical associations with dementia risk in prior epidemiological studies. Emerging evidence suggests that survival bias—where premature mortality in individuals with sleep difficulty obscures dementia risk—may explain these inconsistencies. We analysed data from 457,367 UK Biobank participants aged 40–69 years who were enrolled at baseline between 2006 and 2010 and followed until 2022. Sleep difficulty was assessed via self-reported questionnaires, and dementia was obtained from electronic health records. To address survival bias, we employed a semi-competing risks framework that jointly models dementia incidence and mortality, contrasting results with conventional Cox proportional hazard models. Semi-competing risk analyses suggested that usual sleep difficulty modestly increased risks of vascular dementia (fully adjusted HR 1.14, 95% CI 1.02–1.28) and slightly increased all-cause dementia (HR 1.03, 95% CI 0.98–1.08) in the total sample. The association is particularly strong in younger adults (below 55 years old) and low APOE risk (APOE ε4 non-carriers) when adjusted for age, sex and education. Conversely, Cox models suggested a protective association between usual sleep difficulty and all-cause dementia in the total sample, aligning with prior UK Biobank studies. This discrepancy highlights how survival bias distorts risk estimates. Our findings resolve conflicting evidence by demonstrating sleep difficulty’s direct dementia risk when accounting for competing mortality. The semi-competing risks approach provides a robust framework for ageing research, when survival bias is present. Clinically, these results underscore sleep difficulty management as a modifiable dementia prevention target in mid-to-late life.


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作者介绍:

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孙韬,中国人民大学统计学院副教授,博士毕业于匹兹堡大学生物统计系。研究方向包括复杂生存数据建模、老年失能失智风险管理、基于神经网络的复杂疾病风险预测。主持国家自然科学基金青年项目和面上项目,全国统计科学研究重点项目。

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蒋鑫钰,中国人民大学统计学院2023级博士研究生,主要研究方向为老年健康风险管理,人寿与健康保险定价,复杂生存数据的建模与应用等。

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王晓军,中国人民大学吴玉章讲席教授,统计学院教授。国家统计专家咨询委员会委员,兼任中国统计教育学会副会长、国务院学位委员会统计学学科评议组召集人、全国应用统计专业学位研究生教育指导委员会秘书长、国家统计局-中国人民大学数据开发中心执行主任。主要从事人口统计、人寿保险、养老金、社会保障以及与健康相关的风险量化模型与应用研究。