Journal of Personalized Medicine (Dec 2021)

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

DOI
https://doi.org/10.3390/jpm11010014
Journal volume & issue
Vol. 11, no. 14
p. 14

Abstract

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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.

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