Combinatorial Markers of Mild Cognitive Impairment Conversion to Alzheimer's Disease - Cytokines and MRI Measures Together Predict Disease Progression
Combinatorial Markers of Mild Cognitive Impairment Conversion to Alzheimer's Disease - Cytokines and MRI Measures Together Predict Disease Progression
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DOI：
10.3233/jad-2011-0044
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发表时间：
2011-01-01
影响因子：
4
通讯作者：
Lovestone, Simon
中科院分区：
文献类型：
--
作者：
Furney, Simon J.;Kronenberg, Deborah;Lovestone, Simon
轻度认知障碍 (MCI) 患者是否会发展为痴呆症尚不确定，临床医生也无法预测哪些人最有可能转变为痴呆症。临床医生无法预测进展，限制了 MCI 作为一种综合征在预防试验中的治疗，并且随着越来越多的人在记忆诊所出现这种综合征，并且早期诊断是卫生服务的主要目标，这提出了一个重要的临床问题。一些数据表明，CSF 生物标志物和使用 PET 的功能成像可能作为标志物来促进转化预测。然而，这两种技术都很昂贵并且并不普遍可用。我们研究的目的是调查结合常规临床实践中更容易获得的生物标志物来预测从 MCI 向阿尔茨海默病的转化的潜在附加益处。为了探索这一点，我们将结构 MRI 的自动区域分析与血浆细胞因子和趋化因子的分析相结合，并将其与 APOE 基因型测量和临床评估进行比较，以评估哪种方法最能预测进展。在总共 205 名 MCI 患者中，其中 77 人随后转化为阿尔茨海默氏病，我们发现炎症的生化标记物比 APOE 基因型或临床测量更能预测转化（曲线下面积 (AUC) 分别为 0.65、0.62、0.59）。在也进行 MRI 扫描的受试者子集中，炎症血清标志物和 MRI 自动成像分析的组合提供了最佳的转化预测因子（AUC 0.78）。这些结果表明，与单独的数据类型、APOE 基因型或临床数据相比，成像和细胞因子生物标志物的组合提供了对 MCI 向 AD 转化的预测的改进，并且预测的准确性具有临床实用性。
Progression of people presenting with Mild Cognitive Impairment (MCI) to dementia is not certain and it is not possible for clinicians to predict which people are most likely to convert. The inability of clinicians to predict progression limits the use of MCI as a syndrome for treatment in prevention trials and, as more people present with this syndrome in memory clinics, and as earlier diagnosis is a major goal of health services, this presents an important clinical problem. Some data suggest that CSF biomarkers and functional imaging using PET might act as markers to facilitate prediction of conversion. However, both techniques are costly and not universally available. The objective of our study was to investigate the potential added benefit of combining biomarkers that are more easily obtained in routine clinical practice to predict conversion from MCI to Alzheimer's disease. To explore this we combined automated regional analysis of structural MRI with analysis of plasma cytokines and chemokines and compared these to measures of APOE genotype and clinical assessment to assess which best predict progression. In a total of 205 people with MCI, 77 of whom subsequently converted to Alzheimer's disease, we find biochemical markers of inflammation to be better predictors of conversion than APOE genotype or clinical measures ( Area under the curve (AUC) 0.65, 0.62, 0.59 respectively). In a subset of subjects who also had MRI scans the combination of serum markers of inflammation and MRI automated imaging analysis provided the best predictor of conversion ( AUC 0.78). These results show that the combination of imaging and cytokine biomarkers provides an improvement in prediction of MCI to AD conversion compared to either datatype alone, APOE genotype or clinical data and an accuracy of prediction that would have clinical utility.