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AD Blank Spot Model for Evaluation of Alzheimer’s Disease

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Handbook of Computational Neurodegeneration

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Abstract

The discovery of biomarkers that confer high confidence of Alzheimer’s disease (AD) diagnosis and disease management would be a valuable tool to study the etiology of AD, to find risk factors, and to discover more treatments and medicines. With increased knowledge of various pathways that play significant roles in AD and other factors, it has become clear that one biomarker profile is not enough to identify differentially expressed proteins between AD patients and control individuals and provide conclusive diagnostic results. Hence, the aim of this chapter is to provide a mechanistic survey of the many complex and heterogeneous factors that may result in the different phenotypes of AD. In this context, AD Blank Spot is a wide-open system that uses data mining, machine learning, and artificial intelligence techniques, and has the potential to stage the individuals at risk or with AD according to their state of disease progression. Each of these factors have specific pathological roles in the molecular and cellular processes of this multifactorial disease and point at the biomarkers that should be valued for creating precise disease phenotypes.

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Abbreviations

ABCA7:

ATP-binding cassette subfamily A member 7

Ach:

Acetylcholine

AD:

Alzheimer’s disease

APOE:

Apolipoprotein E

APP:

Amyloid precursor protein

BBB:

Blood–brain barrier

BIN1:

Bridging integrator 1

CLU:

Clusterin

CNS:

Central nervous system

CR1:

Complement component receptor 1

CSF:

Cerebrospinal fluid

EOAD:

Early-onset Alzheimer’s disease

ER:

Endoplasmic reticulum

FINGER:

Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability

GSK3:

Glycogen synthase kinase-3

HLA-DRB1/HLA-DRB5:

Major histocompatibility complex class II DRβ1 and 5

IL:

Interleukin

LOAD:

Late-onset Alzheimer’s disease

MAPT:

Microtubule-associated protein tau

MMSE:

Modified Mini-Mental State Exam

MRI:

Magnetic resonance imaging

NFTs:

Neurofibrillary tangles

NMDAR:

N-methyl-D-aspartate receptors

OAA:

Oxaloacetate

PA:

Physical activity

PET:

Positron emission tomography

PICALM:

Phosphatidylinositol binding clathrin assembly protein

PSEN1:

Presenilin 1

PSEN2:

Presenilin 2

RCT:

Randomized controlled trial

ROS:

Reactive oxygen species

SORL1:

Sortilin-related receptor 1

T2D:

Type 2 diabetes

TNF:

Tumor necrosis factor

TREM2:

Triggering receptor expressed on myeloid cells 2

VD:

Vascular dementia

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Author information

Authors and Affiliations

  1. Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece

    Antigoni Avramouli & Panagiotis M. Vlamos

Authors
  1. Antigoni Avramouli
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  2. Panagiotis M. Vlamos
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Corresponding author

Correspondence to Antigoni Avramouli .

Editor information

Editors and Affiliations

  1. Department of Informatics, Ionian University Department of Informatics, Corfu, Greece

    Panayiotis Vlamos

  2. Wilfrid Laurier University, Brantford, Canada

    Ilias S. Kotsireas

  3. ResearchGate, ETH Zurich ResearchGate, Zurich, Zürich, Switzerland

    Ioannis Tarnanas

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Avramouli, A., Vlamos, P.M. (2023). AD Blank Spot Model for Evaluation of Alzheimer’s Disease. In: Vlamos, P., Kotsireas, I.S., Tarnanas, I. (eds) Handbook of Computational Neurodegeneration. Springer, Cham. https://doi.org/10.1007/978-3-319-75479-6_58-1

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  • DOI: https://doi.org/10.1007/978-3-319-75479-6_58-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75479-6

  • Online ISBN: 978-3-319-75479-6

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