Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 Xinjiannanlu Street, Taiyuan, China.
Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China.
Psychiatry Res. 2019 Aug;278:19-26. doi: 10.1016/j.psychres.2019.05.027. Epub 2019 May 16.
There is a pressing need to identify individuals at high risk of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) based on available repeated cognitive measures in primary care. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we applied a joint latent class mixed model (JLCM) to derive a 3-class solution: low risk (72.65%), medium risk (20.41%) and high risk (6.94%). In the low-risk group, individuals with lower daily activity and ApoEε4 carriers were at greater risk of conversion from MCI to AD. In the medium-risk group, being female, single, and an ApoEε4 carrier increased risk of conversion to AD. In the high-risk group, individuals with lower education level and single individuals were at greater risk of conversion to AD. Individual dynamic prediction for conversion from MCI to AD after 10 years was derived. Accurate identification of conversion from MCI to AD contributes to earlier close monitoring, appropriate management, and targeted interventions. Thereby, it can reduce avoidable hospitalizations for the high-risk MCI population. Moreover, it can avoid expensive follow-up tests that may provoke unnecessary anxiety for low-risk individuals and their families.
目前迫切需要根据初级保健中可用的重复认知测量结果，识别出从轻度认知障碍（MCI）向阿尔茨海默病（AD）转化的高危个体。利用阿尔茨海默病神经影像学倡议（ADNI）的数据，我们应用联合潜在类别混合模型（JLCM）得出了一个 3 类解决方案：低风险（72.65%）、中风险（20.41%）和高风险（6.94%）。在低风险组中，日常活动水平较低和载脂蛋白 Eε4 携带者的个体向 AD 转化的风险更高。在中风险组中，女性、单身和载脂蛋白 Eε4 携带者的个体向 AD 转化的风险增加。在高风险组中，教育水平较低和单身的个体向 AD 转化的风险更高。得出了个体从 MCI 向 AD 转化 10 年后的动态预测。准确识别从 MCI 向 AD 的转化有助于更早地进行密切监测、适当的管理和有针对性的干预。从而可以减少高危 MCI 人群的不必要住院。此外，它可以避免对低风险个体及其家属进行昂贵的后续检查，这些检查可能会引起不必要的焦虑。
