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Machine Learning Profiling of Alzheimer's Disease Patients Based on Current Cerebrospinal Fluid Markers and Iron Content in Biofluids

Authors
  • Ficiarà, Eleonora1
  • Boschi, Silvia1, 2
  • Ansari, Shoeb1
  • D'Agata, Federico1
  • Abollino, Ornella3
  • Caroppo, Paola4
  • Di Fede, Giuseppe4
  • Indaco, Antonio4
  • Rainero, Innocenzo1
  • Guiot, Caterina1
  • 1 Department of Neurosciences “Rita Levi Montalcini”, University of Torino, Torino , (Italy)
  • 2 Department NEUROFARBA, University of Firenze, Firenze , (Italy)
  • 3 Department of Drug Science and Technology, University of Torino, Torino , (Italy)
  • 4 Unit of Neurology 5 and Neuropathology, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan , (Italy)
Type
Published Article
Journal
Frontiers in Aging Neuroscience
Publisher
Frontiers Media SA
Publication Date
Feb 22, 2021
Volume
13
Identifiers
DOI: 10.3389/fnagi.2021.607858
Source
Frontiers
Keywords
Disciplines
  • Neuroscience
  • Original Research
License
Green

Abstract

Alzheimer's disease (AD) is the most common form of dementia, characterized by a complex etiology that makes therapeutic strategies still not effective. A true understanding of key pathological mechanisms and new biomarkers are needed, to identify alternative disease-modifying therapies counteracting the disease progression. Iron is an essential element for brain metabolism and its imbalance is implicated in neurodegeneration, due to its potential neurotoxic effect. However, the role of iron in different stages of dementia is not clearly established. This study aimed to investigate the potential impact of iron both in cerebrospinal fluid (CSF) and in serum to improve early diagnosis and the related therapeutic possibility. In addition to standard clinical method to detect iron in serum, a precise quantification of total iron in CSF was performed using graphite-furnace atomic absorption spectrometry in patients affected by AD, mild cognitive impairment, frontotemporal dementia, and non-demented neurological controls. The application of machine learning techniques, such as clustering analysis and multiclassification algorithms, showed a new potential stratification of patients exploiting iron-related data. The results support the involvement of iron dysregulation and its potential interaction with biomarkers (Tau protein and Amyloid-beta) in the pathophysiology and progression of dementia.

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