Background Alzheimer’s disease (AD) affects over 6 million people and is the seventh-leading cause of death in the US. This study compares wrist-worn accelerometry-derived PA measures against traditional risk factors for incident AD in the UK Biobank. Methods Of 42,157 UK Biobank participants 65 years and older who had accelerometry data and no prior AD diagnosis, 157 developed AD by April 1, 2021 (264,988 person-years or on average 6.2 years of follow-up). 12 traditional predictors and 8 accelerometer-based PA measures were used in single- and multivariate Cox models. Their predictive performances for future AD diagnosis were compared across models using the repeated cross-validated concordance (rcvC). To account for potential reverse causality, sensitivity analyses were conducted by removing dropouts and cases within the first six months, one year, and two years. Results The best-performing individual predictors of incident AD were age (p < 0.0001, rcvC = 0.658) and moderate-to-vigorous PA (MVPA, p = 0.0001, rcvC = 0.622). Forward selection produced a model that included age, diabetes, and MVPA, rcvC = 0.681). Adding MVPA to the model containing age and diabetes improved its rcvC from 0.665 to 0.681 (p = 0.0030), more than all other potential risk factors considered. Conclusion Objective PA summaries are the best single predictors among modifiable risk factors with a predictive performance close to that of age. Adding PA summaries to traditional risk factors for AD substantially increases the predictive performance of these models. Increasing MVPA by 14.5 minutes/day reduces the hazard substantially, equivalent to two years younger.

中文翻译：

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使用腕戴式加速度计客观测量身体活动作为英国生物库中阿尔茨海默病事件的预测指标


背景 阿尔茨海默病 （AD） 影响着超过 600 万人，是美国第七大死因。本研究将腕戴式加速度计衍生的 PA 措施与英国生物样本库中 AD 事件的传统危险因素进行了比较。方法 在 42,157 名 65 岁及以上有加速度测量数据且既往无 AD 诊断的英国生物样本库参与者中，到 2021 年 4 月 1 日，有 157 人患上了 AD（264,988 人年或平均 6.2 年的随访）。12 个传统预测因子和 8 个基于加速度计的 PA 测量用于单变量和多变量 Cox 模型。使用重复交叉验证一致性 （rcvC） 在模型之间比较他们对未来 AD 诊断的预测性能。为了解释潜在的反向因果关系，通过去除前 6 个月、 1 年和 2 年内的辍学和病例进行敏感性分析。结果 AD 事件表现最好的个体预测因子是年龄 （p < 0.0001， rcvC = 0.658） 和中度至重度 PA （MVPA， p = 0.0001， rcvC = 0.622）。前向选择产生了一个包括年龄、糖尿病和 MVPA 的模型，rcvC = 0.681）。将 MVPA 添加到包含年龄和糖尿病的模型中，将其 rcvC 从 0.665 提高到 0.681 （p = 0.0030），高于考虑的所有其他潜在风险因素。结论 客观 PA 总结是可改变风险因素中最好的单一预测因子，预测性能接近年龄。将 PA 摘要添加到 AD 的传统风险因素中可显著提高这些模型的预测性能。将 MVPA 增加 14.5 分钟/天可显著降低危险，相当于年轻两岁。