Center for Computer Machine/Human Intelligence Networking and Distributed Systems, University of Massachusetts, Lowell, MA, USA.
J Alzheimers Dis. 2022;87(2):583-594. doi: 10.3233/JAD-215640.
Structural brain imaging metrics and gene expression biomarkers have previously been used for Alzheimer's disease (AD) diagnosis and prognosis, but none of these studies explored integration of imaging and gene expression biomarkers for predicting mild cognitive impairment (MCI)-to-AD conversion 1-2 years into the future.
We investigated advantages of combining gene expression and structural brain imaging features for predicting MCI-to-AD conversion. Selection of the differentially expressed genes (DEGs) for classifying cognitively normal (CN) controls and AD patients was benchmarked against previously reported results.
The current work proposes integrating brain imaging and blood gene expression data from two public datasets (ADNI and ANM) to predict MCI-to-AD conversion. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated in the two independents patient cohorts.
Combining DEGs and imaging biomarkers for predicting MCI-to-AD conversion yielded 0.832-0.876 receiver operating characteristic (ROC) area under the curve (AUC), which exceeded the 0.808-0.840 AUC from using the imaging features alone. With using only three DEGs, the CN versus AD predictive model achieved 0.718, 0.858, and 0.873 cross-validation AUC for the ADNI, ANM1, and ANM2 datasets.
For the first time we show that combining gene expression and imaging biomarkers yields better predictive performance than using imaging metrics alone. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated to produce consistent results in the two independents patient cohorts. Using an improved feature selection, we show that predictive models with fewer gene expression probes can achieve competitive performance.
结构脑影像学指标和基因表达生物标志物以前曾被用于阿尔茨海默病（AD）的诊断和预后，但这些研究都没有探索将影像学和基因表达生物标志物结合起来预测轻度认知障碍（MCI）向 AD 转化的情况。
我们研究了结合基因表达和结构脑影像学特征来预测 MCI 向 AD 转化的优势。为了对正常认知（CN）对照组和 AD 患者进行分类，对差异表达基因（DEGs）的选择与之前报道的结果进行了基准测试。
目前的工作提出了一种整合来自两个公共数据集（ADNI 和 ANM）的脑成像和血液基因表达数据的方法，以预测 MCI 向 AD 转化。提出并评估了一种用于整合来自多个平台的基因表达数据的新方法，该方法应用于两个独立的患者队列中。
结合 DEGs 和影像学生物标志物预测 MCI 向 AD 转化的受试者工作特征（ROC）曲线下面积（AUC）为 0.832-0.876，优于单独使用影像学特征的 0.808-0.840 AUC。仅使用三个 DEGs，CN 与 AD 的预测模型在 ADNI、ANM1 和 ANM2 数据集的交叉验证 AUC 分别为 0.718、0.858 和 0.873。
这是首次表明，与单独使用影像学指标相比，结合基因表达和影像学生物标志物可获得更好的预测性能。提出并评估了一种用于整合来自多个平台的基因表达数据的新方法，以在两个独立的患者队列中产生一致的结果。使用改进的特征选择，我们表明，具有较少基因表达探针的预测模型可以实现有竞争力的性能。
