Eli Lilly and Company, Indianapolis, IN, USA.
J Alzheimers Dis. 2012;32(2):373-85. doi: 10.3233/JAD-2012-120832.
The goal of this study was to identify the optimal combination of magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) biomarkers to predict conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) dementia within two years, for enriching clinical trial populations. Data from 63 subjects in the Alzheimer's Disease Neuroimaging Initiative aMCI cohort who had MRI and FDG-PET imaging along with CSF data at baseline and at least two years clinical follow-up were used. A Bayesian classification method was used to determine which combination of 31 variables (MRI, FDG-PET, CSF measurements, apolipoprotein E (ApoE) genotype, and cognitive scores) provided the most accurate prediction of aMCI to AD conversion. The cost and time trade-offs for the use of these biomarkers as inclusion criteria in clinical trials were evaluated. Using the combination of all biomarkers, ApoE genotype, and cognitive scores, we achieved an accuracy of 81% in predicting aMCI to AD conversion. With only ApoE genotype and cognitive scores, the prediction accuracy decreased to 62%. By comparing individual modalities, we found that MRI measures had the best predictive power (accuracy = 78%), followed by ApoE, FDG-PET, CSF, and the Alzheimer's disease assessment scale-cognitive subscale. The combination of biomarkers from different modalities, measuring complementary aspects of AD pathology, provided the most accurate prediction of aMCI to AD conversion within two years. This was predominantly driven by MRI measures, which emerged as the single most powerful modality. Overall, the combination of MRI, ApoE, and cognitive scores provided the best trade-off between cost and time compared with other biomarker combinations for patient recruitment in clinical trial.
本研究的目的是确定磁共振成像（MRI）、[18F]-氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）和脑脊液（CSF）生物标志物的最佳组合，以预测两年内从遗忘型轻度认知障碍（aMCI）向阿尔茨海默病（AD）痴呆的转化，从而丰富临床试验人群。使用了阿尔茨海默病神经影像学倡议（ADNI）aMCI 队列中 63 名受试者的数据，这些受试者在基线时和至少两年的临床随访时进行了 MRI 和 FDG-PET 成像以及 CSF 数据采集。使用贝叶斯分类方法来确定 31 个变量（MRI、FDG-PET、CSF 测量值、载脂蛋白 E（ApoE）基因型和认知评分）的哪种组合可以最准确地预测 aMCI 向 AD 的转化。评估了将这些生物标志物用作临床试验纳入标准的成本和时间权衡。使用所有生物标志物、ApoE 基因型和认知评分的组合，我们在预测 aMCI 向 AD 转化方面达到了 81%的准确率。仅使用 ApoE 基因型和认知评分，预测准确率下降至 62%。通过比较各个模态，我们发现 MRI 测量具有最佳的预测能力（准确率=78%），其次是 ApoE、FDG-PET、CSF 和阿尔茨海默病评估量表-认知子量表。来自不同模态的生物标志物的组合，测量 AD 病理的互补方面，提供了最准确的两年内 aMCI 向 AD 转化的预测。这主要是由 MRI 测量驱动的，它成为了最强大的单一模态。总体而言，与其他生物标志物组合相比，MRI、ApoE 和认知评分的组合在成本和时间方面为临床试验中的患者招募提供了最佳的权衡。
