A Conformation Variant of p53 Combined With Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages
Journal of Personalized Medicine
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© 2020 by the authors. Li-censee MDPI, Basel, Switzerland. Early diagnosis of Alzheimer’s disease (AD) is a crucial starting point in disease man-agement. 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 rec-ognized 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.
Alzheimer’s disease; Blood-based biomarker; Conformation variant of p53; Machine learning; β-amyloid
Cognitive Neuroscience | Medical Biotechnology
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A Conformation Variant of p53 Combined With Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages.
Journal of Personalized Medicine, 11(1),