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首页> 外文期刊>NeuroImage: Clinical >Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease
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Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease

机译：数据驱动疾病进展模型的多研究验证，以表征阿尔茨海默病生物标志物演变

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Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols.Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used.Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2EBM?=?0.866; R2DEBM?=?0.906). In discriminant analyses, significant differences (p-value?≤?0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value?>?0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available.Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain.
机译：了解沿阿尔茨海默病过程的生物和临床事件序列提供了患有痴呆病理生理学的见解，可以帮助参与者选择临床试验。我们的目标是培训两个数据驱动的计算模型，用于在特征良好的研究数据的基础上列出这些事件，基于事件的模型（EBM）和鉴别 - EBM（DEBM），然后验证临床主题的受过培训的模型以较少结构的数据采集协议为特征的群组被认为是综合性数据群组总计2389认知正常（CN），1424名轻度认知障碍（MCI）和743 Alzheimer疾病（AD）患者。 Alzheimer的疾病神经影像倡议（ADNI）数据集被用作疾病模型的收缩训练，而多合一体数据群组用作测试集以进行验证。使用与临床，认知，成像和脑脊液（CSF）生物标志物相关的横截面信息。用EBM和DEBM获得的序列在单一生物标志物的排序中显示出差异，但根据第一个生物标志物变得异常是与之相关的那些CSF，其次是认知评分，而结构性成像显示出显着的体积减少在疾病进展的后期。基于用两种模型获得的序列的测试设定对象的分期显示出与迷你精神状态检查评分的良好线性相关性（R2EBM？= 0.866; R2DEBM？= 0.906）。在判别分析中，在两种模型中观察到来自训练和测试集的受试者的分期之间的显着差异（p值？≤≤0.05）。观察来自训练和试验的受试者的分期之间没有显着差异（p值？> 0.05），当考虑由562个受试者组成的子集，所有生物标志物家族（认知，成像和CSF）可用。获得的序列DEBM以数据驱动的方式重新承载启发式模型，并且是临床合理的。我们展示了两种疾病进展模型的互补性及其在检测广告阶段的鲁棒性。这是将数据驱动统计模型采用到临床领域的重要一步。

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