Combination of 18F-FDG PET and Cerebrospinal Fluid Biomarkers as a Better Predictor of the Progression to Alzheimer's Disease in Mild Cognitive Impairment Patients
Combination of 18F-FDG PET and Cerebrospinal Fluid Biomarkers as a Better Predictor of the Progression to Alzheimer's Disease in Mild Cognitive Impairment Patients
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DOI：
10.3233/jad-2012-121489
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发表时间：
2013-01-01
影响因子：
4
通讯作者：
Nordberg, Agneta
中科院分区：
文献类型：
--
作者：
Choo, Il Han;Ni, Ruiqing;Nordberg, Agneta
针对阿尔茨海默病（AD）谱系提出了基于生物标志物的新诊断标准。然而，目前尚不清楚任何生物标志物单独具有令人满意的 AD 预测能力。我们探索了基线人口统计学、神经心理学、F-18-氟脱氧葡萄糖正电子发射断层扫描 (FDG-PET)、脑脊液 (CSF) 生物标志物和载脂蛋白 E (APOE) 基因型评估的最佳组合模型，以预测轻度认知障碍 (MCI) 患者进展为 AD。对 MCI 患者进行纵向临床随访（平均 44 个月；范围 1.6-161.7 个月）。在 83 名 MCI 患者中，26 名进展为 AD（MCI-AD），51 名未恶化（MCI-Stable）。我们应用单变量和多变量逻辑回归分析，以及包括生物标志物在内的 AD 预测因子的多步骤模型选择。在单变量逻辑分析中，我们选择年龄、Rey 听觉言语保留测试、顶叶葡萄糖代谢率、CSF 总 tau 蛋白以及是否存在至少一个 APOE epsilon 4 等位基因作为预测因子。通过多变量逐步逻辑分析和模型选择，我们发现顶叶葡萄糖代谢率和总tau蛋白的组合代表了AD预测的最佳模型。总之，我们的研究结果强调，通过 PET 进行区域葡萄糖代谢评估和脑脊液生物标志物评估相结合，可以显着提高每种方法的 AD 预测诊断准确性。
The biomarker-based new diagnostic criteria have been proposed for Alzheimer's disease (AD) spectrum. However, any biomarker alone has not been known to have satisfactory AD predictability. We explored the best combination model with baseline demography, neuropsychology, F-18-fluorodeoxyglucose positron emission tomography (FDG-PET), cerebrospinal fluid (CSF) biomarkers, and apolipoprotein E (APOE) genotype evaluation to predict progression to AD in mild cognitive impairment (MCI) patients. Alongitudinal clinical follow-up (mean, 44 months; range, 1.6-161.7 months) of MCI patients was done. Among 83 MCI patients, 26 progressed to AD (MCI-AD) and 51 did not deteriorate (MCI-Stable). We applied that univariate and multivariate logistic regression analyses, and multistep model selection for AD predictors including biomarkers. In univariate logistic analysis, we selected age, Rey Auditory Verbal Retention Test, parietal glucose metabolic rate, CSF total tau, and presence or not of at least one APOE epsilon 4 allele as predictors. Through multivariate stepwise logistic analysis and model selection, we found the combination of parietal glucose metabolic rate and total tau representing the best model for AD prediction. In conclusion, our findings highlight that the combination of regional glucose metabolic assessment by PET and CSF biomarkers evaluation can significantly improve AD predictive diagnostic accuracy of each respective method.