Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
J Alzheimers Dis. 2018;63(4):1383-1393. doi: 10.3233/JAD-170769.
There are no agreed-upon variables for predicting progression from unimpaired cognition to amnestic mild cognitive impairment (aMCI), or from aMCI to Alzheimer's disease (AD).
Use ADNI data to develop a 'Framingham-like' prediction model for a 4-year period.
We developed models using the strongest baseline predictors from six domains (demographics, neuroimaging, CSF biomarkers, genetics, cognitive tests, and functional ability). We chose the best predictor from each domain, which was dichotomized into more versus less harmful.
There were 224 unimpaired individuals and 424 aMCI subjects with baseline data on all predictors, of whom 37 (17% ) and 150 (35% ) converted to aMCI and AD, respectively, during 4 years of follow-up. For the unimpaired, CSF tau/Aβ ratio, hippocampal volume, and a memory score predicted progression. For those aMCI at baseline, the same predictors plus APOE4 status and functional ability predicted progression. Demographics and family history were not important predictors for progression for either group. The fit statistic was good for the unimpaired-aMCI model (C-statistic 0.80) and very good for the aMCI-AD model (C-statistic 0.91). Among the unimpaired, those with no harmful risk factors had a 4-year predicted 2% risk of progression, while those with the most harmful risk factors had a predicted 35% risk. The aMCI subjects with no harmful risk factors had a predicted 1% risk of progression those with all six harmful risk factors had a predicted 90% risk.
Our parsimonious model accurately predicted progression from unimpaired to aMCI with three variables, and from aMCI to AD with five variables.
目前尚无公认的变量可用于预测认知正常向遗忘型轻度认知障碍（aMCI），或从 aMCI 向阿尔茨海默病（AD）的进展。
利用 ADNI 数据建立一个为期 4 年的“弗雷明汉式”预测模型。
我们使用来自六个领域（人口统计学、神经影像学、CSF 生物标志物、遗传学、认知测试和功能能力）的最强基线预测因子建立模型。我们从每个领域中选择最佳预测因子，将其分为更有害和不那么有害的两类。
共有 224 名认知正常者和 424 名 aMCI 患者在所有预测因子的基线数据上，其中 37 名（17%）和 150 名（35%）在 4 年的随访中分别转化为 aMCI 和 AD。对于认知正常者，CSF tau/Aβ 比值、海马体积和记忆评分可预测进展。对于基线时的 aMCI 患者，相同的预测因子加上 APOE4 状态和功能能力可预测进展。对于两组患者，人口统计学和家族史都不是进展的重要预测因子。对于认知正常者向 aMCI 的模型，拟合统计量较好（C 统计量为 0.80），而对于 aMCI 向 AD 的模型，拟合统计量非常好（C 统计量为 0.91）。在认知正常者中，没有有害风险因素的患者 4 年预测进展的风险为 2%，而有最多有害风险因素的患者预测进展的风险为 35%。没有有害风险因素的 aMCI 患者 4 年预测进展的风险为 1%，而有所有 6 个有害风险因素的患者预测进展的风险为 90%。
我们的简约模型使用三个变量准确预测了认知正常者向 aMCI 的进展，使用五个变量预测了 aMCI 向 AD 的进展。
