Kim Young Ju, Kim Si Eun, Hahn Alice, Jang Hyemin, Kim Jun Pyo, Kim Hee Jin, Na Duk L, Chin Juhee, Seo Sang Won
Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.
Front Aging Neurosci. 2023 Mar 13;15:1122927. doi: 10.3389/fnagi.2023.1122927. eCollection 2023.
Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts.
A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model.
Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model).
Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.
识别有认知功能下降倾向的认知未受损（CU）个体，将有助于预防阿尔茨海默病（AD）的工作。因此，我们旨在开发一个模型，以预测两个独立队列中CU个体的认知衰退情况。
本研究招募了来自阿尔茨海默病神经影像倡议（ADNI）的407名CU个体和来自三星医疗中心（SMC）的285名CU个体。我们在ADNI和SMC队列中使用神经心理学综合评分评估认知结果。我们进行了潜在增长混合模型分析并开发了预测模型。
增长混合模型分别在ADNI和SMC队列中识别出13.8%和13.0%的CU个体为“衰退组”。在ADNI队列中，多变量逻辑回归模型显示，淀粉样蛋白-β（Aβ）摄取增加（β[标准误]：4.852[0.862]，P<0.001）、基线认知综合评分较低（β[标准误]：-0.274[0.070]，P<0.001）以及海马体积减小（β[标准误]：-0.952[0.302]，P=0.002）可预测认知衰退。在SMC队列中，Aβ摄取增加（β[标准误]：2.007[0.549]，P<0.001）和基线认知综合评分较低（β[标准误]：-4.464[0.758]，P<0.001）可预测认知衰退。最后，认知衰退预测模型显示出良好到出色的区分能力和校准能力（ADNI模型的C统计量为0.85，SMC模型的C统计量为0.94）。
我们的研究为CU个体的认知轨迹提供了新的见解。此外，该预测模型有助于在未来的一级预防试验中对CU个体进行分类。
