King's College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, London, UK.
NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK.
Sci Adv. 2019 Feb 6;5(2):eaau7220. doi: 10.1126/sciadv.aau7220. eCollection 2019 Feb.
A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer's disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as Aβ negative or Aβ positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict Aβ-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting Aβ-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.
基于血液的临床前疾病评估在阿尔茨海默病（AD）治疗试验的参与者富集方面具有巨大潜力。在这项研究中，AIBL 和 KARVIAH 队列中的认知正常个体通过正电子发射断层扫描（PET）被定义为 Aβ阴性或 Aβ阳性。非靶向蛋白质组学分析采用肽分级和高分辨率质谱定量所有参与者的血浆样本中的相对蛋白质丰度。使用特征选择和机器学习在 AIBL 中训练蛋白质分类器模型来预测 Aβ阳性参与者，并在 KARVIAH 中进行独立评估。建立了一个用于预测 Aβ阳性参与者的 12 特征模型，表现出较高的准确性（测试中接收者操作特征曲线下的面积= 0.891，敏感性= 0.78，特异性= 0.77）。这项广泛的血浆蛋白质组学研究无偏地强调了 AD 病理的假定和新候选物，这些候选物应该通过自动化方法进一步验证。
