Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease
Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease
复制标题
DOI：
10.1016/j.nbd.2019.01.003
复制
发表时间：
2019-04-01
影响因子：
6.1
通讯作者：
Eggers, Carsten
中科院分区：
文献类型：
--
作者：
Glaab, Enrico;Trezzi, Jean-Pierre;Eggers, Carsten
背景：帕金森病（PD）的诊断通常仍然是一个临床挑战。分子神经影像学可以促进诊断过程。代谢组学特征的诊断潜力最近已得到认识。方法：我们研究了通过机器学习对血液代谢组学和 PET 成像进行联合数据分析是否可以增强诊断辨别力并提供进一步的病理生理学见解。从 60 名 PD 患者和 15 名年龄和性别匹配的健康对照中采集血浆样本。我们通过气相色谱-质谱联用 (GC-MS) 确定了代谢组学特征。在同一队列中，我们同时对 44 名患者和 14 名对照者进行了 FDOPA PET，对 51 名患者和 16 名对照者进行了 FDG PET。一年后，18 名 PD 患者接受了随访检查。这两个数据集均通过两种机器学习方法进行分析，在留一交叉验证方案中应用线性支持向量机或随机森林，并计算受试者工作特征（ROC）曲线。结果：在代谢组学数据中，病例和对照之间的基线比较以及患者的后续评估表明与氧化应激和炎症相关的代谢变化。对于 FDOPA 和 FDG PET 数据，将成像特征与代谢组学数据相结合时，ROC 分析中的诊断预测性能 (DPP) 最高（最佳 FDOPA + 代谢组学模型的 ROC AUC：0.98；最佳 FDG + 代谢组学模型的 AUC：0.91）。仅使用 PET 属性或仅使用代谢组学特征时，DPP 较低。结论：将血液代谢组学数据与 PET 数据相结合可显着增强诊断辨别力。代谢组学特征还表明细胞过程中有趣的疾病固有变化，包括氧化应激反应和炎症。
Background: The diagnosis of Parkinson's disease (PD) often remains a clinical challenge. Molecular neuroimaging can facilitate the diagnostic process. The diagnostic potential of metabolomic signatures has recently been recognized.Methods: We investigated whether the joint data analysis of blood metabolomics and PET imaging by machine learning provides enhanced diagnostic discrimination and gives further pathophysiological insights. Blood plasma samples were collected from 60 PD patients and 15 age- and gender-matched healthy controls. We determined metabolomic profiles by gas chromatography coupled to mass spectrometry (GC-MS). In the same cohort and at the same time we performed FDOPA PET in 44 patients and 14 controls and FDG PET in 51 patients and 16 controls. 18 PD patients were available for a follow-up exam after one year. Both data sets were analysed by two machine learning approaches, applying either linear support vector machines or random forests within a leave-one-out cross-validation scheme and computing receiver operating characteristic (ROC) curves.Results: In the metabolomics data, the baseline comparison between cases and controls as well as the follow-up assessment of patients pointed to metabolite changes associated with oxidative stress and inflammation. For the FDOPA and FDG PET data, the diagnostic predictive performance (DPP) in the ROC analyses was highest when combining imaging features with metabolomics data (ROC AUC for best FDOPA + metabolomics model: 0.98; AUC for best FDG + metabolomics model: 0.91). DPP was lower when using only PET attributes or only metabolomics signatures.Conclusion: Integrating blood metabolomics data combined with PET data considerably enhances the diagnostic discrimination power. Metabolomic signatures also indicate interesting disease-inherent changes in cellular processes, including oxidative stress response and inflammation.