Howlett James, Hill Steven M, Ritchie Craig W, Tom Brian D M
MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Front Big Data. 2021 Aug 20;4:676168. doi: 10.3389/fdata.2021.676168. eCollection 2021.
A key challenge for the secondary prevention of Alzheimer's dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer's Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.
阿尔茨海默病痴呆二级预防面临的一个关键挑战是，需要通过灵敏的认知测试和生物标志物在疾病进程的早期识别个体。欧洲预防阿尔茨海默病痴呆（EPAD）联盟招募参与者进入一项纵向队列研究，目的是为一项概念验证临床试验建立一个预备队列，并生成一个丰富的纵向数据集用于疾病建模。已收集了广泛测量数据，包括认知结果、神经影像学、脑脊液生物标志物、遗传学以及其他临床和环境风险因素，在基线时可获得1828名符合条件参与者的数据，6个月时为1567名，一年随访时为1188名，两年时为383名，三年随访时为89名参与者。我们创新性地将最先进的纵向建模和风险分层方法应用于这些数据，以描述队列中的疾病进展和生物学异质性。具体而言，我们使用纵向特定类别混合效应模型来描述不同的临床疾病轨迹，并采用半监督贝叶斯聚类方法来探索参与者是否可被分层为具有不同认知功能演变模式的同质亚组，同时在基线生物标志物和生物标志物纵向变化率方面具有亚组特异性特征。
