基于非认知测量的风险模型可能识别出无症状的阿尔茨海默病。
Risk Models Based on Non-Cognitive Measures May Identify Presymptomatic Alzheimer's Disease.
机构信息
Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.
Rush Alzheimer's Disease Center, Rush University Medicine Center, Chicago, IL, USA.
出版信息
J Alzheimers Dis. 2022;89(4):1249-1262. doi: 10.3233/JAD-220446.
BACKGROUND
Alzheimer's disease (AD) is a progressive disorder without a cure. Develop risk prediction models for detecting presymptomatic AD using non-cognitive measures is necessary to enable early interventions.
OBJECTIVE
Examine if non-cognitive metrics alone can be used to construct risk models to identify adults at risk for AD dementia and cognitive impairment.
METHODS
Clinical data from older adults without dementia from the Memory and Aging Project (MAP, n = 1,179) and Religious Orders Study (ROS, n = 1,103) were analyzed using Cox proportional hazard models to develop risk prediction models for AD dementia and cognitive impairment. Models using only non-cognitive covariates were compared to models that added cognitive covariates. All models were trained in MAP, tested in ROS, and evaluated by the AUC of ROC curve.
RESULTS
Models based on non-cognitive covariates alone achieved AUC (0.800,0.785) for predicting AD dementia (3.5) years from baseline. Including additional cognitive covariates improved AUC to (0.916,0.881). A model with a single covariate of composite cognition score achieved AUC (0.905,0.863). Models based on non-cognitive covariates alone achieved AUC (0.717,0.714) for predicting cognitive impairment (3.5) years from baseline. Including additional cognitive covariates improved AUC to (0.783,0.770). A model with a single covariate of composite cognition score achieved AUC (0.754,0.730).
CONCLUSION
Risk models based on non-cognitive metrics predict both AD dementia and cognitive impairment. However, non-cognitive covariates do not provide incremental predictivity for models that include cognitive metrics in predicting AD dementia, but do in models predicting cognitive impairment. Further improved risk prediction models for cognitive impairment are needed.
背景
阿尔茨海默病（AD）是一种无法治愈的进行性疾病。使用非认知措施开发用于检测无症状 AD 的风险预测模型对于实现早期干预是必要的。
目的
检查非认知指标是否可以单独用于构建风险模型，以识别有 AD 痴呆和认知障碍风险的成年人。
方法
使用 Cox 比例风险模型分析来自无痴呆老年人的临床数据来自记忆和老化项目（MAP，n=1179）和宗教秩序研究（ROS，n=1103），以开发用于 AD 痴呆和认知障碍的风险预测模型。仅使用非认知协变量的模型与添加认知协变量的模型进行比较。所有模型均在 MAP 中进行训练，在 ROS 中进行测试，并通过 ROC 曲线的 AUC 进行评估。
结果
基于非认知协变量的模型单独预测 AD 痴呆（从基线起 3.5 年）的 AUC（0.800，0.785）。包括额外的认知协变量可提高 AUC（0.916，0.881）。具有单一复合认知评分协变量的模型的 AUC 为（0.905，0.863）。基于非认知协变量的模型单独预测认知障碍（从基线起 3.5 年）的 AUC（0.717，0.714）。包括额外的认知协变量可提高 AUC（0.783，0.770）。具有单一复合认知评分协变量的模型的 AUC 为（0.754，0.730）。
结论
基于非认知指标的风险模型可预测 AD 痴呆和认知障碍。然而，非认知协变量不能为包括认知指标的模型提供对 AD 痴呆的预测性增强，但可以为预测认知障碍的模型提供增强。需要进一步改进用于认知障碍的风险预测模型。
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