A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages
Giulia Abate, Marika Vezzoli, Letizia Polito, Antonio Guaita, Diego Albani, Moira Marizzoni, Emirena Garrafa, Alessandra Marengoni, Gianluigi Forloni, Giovanni B. Frisoni, Jeffrey L. Cummings, Maurizio Memo, Daniela Uberti
Abstract
Early diagnosis of Alzheimer’s disease (AD) is a crucial starting point in disease management. Blood-based biomarkers could represent a considerable advantage in providing AD-risk information in primary care settings. Here, we report new data for a relatively unknown blood-based biomarker that holds promise for AD diagnosis. We evaluate a p53-misfolding conformation recognized by the antibody 2D3A8, also named Unfolded p53 (U-p532D3A8+), in 375 plasma samples derived from InveCe.Ab and PharmaCog/E-ADNI longitudinal studies. A machine learning approach is used to combine U-p532D3A8+ plasma levels with Mini-Mental State Examination (MMSE) and apolipoprotein E epsilon-4 (APOEε4) and is able to predict AD likelihood risk in InveCe.Ab with an overall 86.67% agreement with clinical diagnosis. These algorithms also accurately classify (AUC = 0.92) Aβ+—amnestic Mild Cognitive Impairment (aMCI) patients who will develop AD in PharmaCog/E-ADNI, where subjects were stratified according to Cerebrospinal fluid (CSF) AD markers (Aβ42 and p-Tau). Results support U-p532D3A8+ plasma level as a promising additional candidate blood-based biomarker for AD.