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Open AccessArticle

A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages

1
Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy
2
GolgiCenci Foundation, 20081 Abbiategrasso, Italy
3
Department of Neuroscience, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, 20156 Milan, Italy
4
Laboratory of Alzheimer’s Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy
5
Department of Clinical and Experimental Sciences, University of Brescia, Lombardy, 25123 Brescia, Italy
6
Memory Clinic, University Hospitals and University of Geneva, 1205 Geneva, Switzerland
7
Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV) and Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
8
Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2021, 11(1), 14; https://doi.org/10.3390/jpm11010014
Received: 20 November 2020 / Revised: 22 December 2020 / Accepted: 24 December 2020 / Published: 26 December 2020
(This article belongs to the Special Issue Molecular Biomarkers and Precision Medicine for Alzheimer)
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. View Full-Text
Keywords: blood-based biomarker; Alzheimer’s disease; machine learning; β-amyloid; conformation variant of p53 blood-based biomarker; Alzheimer’s disease; machine learning; β-amyloid; conformation variant of p53
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MDPI and ACS Style

Abate, G.; Vezzoli, M.; Polito, L.; Guaita, A.; Albani, D.; Marizzoni, M.; Garrafa, E.; Marengoni, A.; Forloni, G.; Frisoni, G.B.; Cummings, J.L.; Memo, M.; Uberti, D. A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages. J. Pers. Med. 2021, 11, 14. https://doi.org/10.3390/jpm11010014

AMA Style

Abate G, Vezzoli M, Polito L, Guaita A, Albani D, Marizzoni M, Garrafa E, Marengoni A, Forloni G, Frisoni GB, Cummings JL, Memo M, Uberti D. A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages. Journal of Personalized Medicine. 2021; 11(1):14. https://doi.org/10.3390/jpm11010014

Chicago/Turabian Style

Abate, Giulia; Vezzoli, Marika; Polito, Letizia; Guaita, Antonio; Albani, Diego; Marizzoni, Moira; Garrafa, Emirena; Marengoni, Alessandra; Forloni, Gianluigi; Frisoni, Giovanni B.; Cummings, Jeffrey L.; Memo, Maurizio; Uberti, Daniela. 2021. "A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages" J. Pers. Med. 11, no. 1: 14. https://doi.org/10.3390/jpm11010014

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