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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease
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Somatic Mutations and Alzheimer's Disease.

Jocelyn Downey1, Jacqueline C K Lam1,2, Victor O K Li1

  • 1Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.

Journal of Alzheimer'S Disease : JAD
|September 26, 2022
PubMed
Summary

Somatic mutations accumulating with age may drive Alzheimer's disease (AD) pathogenesis. Research highlights genetic factors and the potential of AI to identify biomarkers and develop treatments for this neurodegenerative condition.

Keywords:
Alzheimer’s diseaseamyloid-β peptideartificial intelligencebig datalate onset Alzheimer’s diseasesomatic mutationstau

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Area of Science:

  • Neuroscience
  • Genetics
  • Computational Biology

Background:

  • Alzheimer's disease (AD) affects 55 million globally, characterized by amyloid-β plaques and tau tangles.
  • While proteinopathy is key, diverse genomic regions and aging contribute significantly to AD neurodegeneration.
  • Accumulating somatic mutations with age, a major AD risk factor, are increasingly implicated in disease initiation.

Purpose of the Study:

  • To review principal genetic factors in Alzheimer's disease pathogenesis.
  • To highlight the contribution of somatic mutations to late-onset AD.
  • To explore the role of AI and big data in biomarker discovery and drug repurposing for AD.

Main Methods:

  • Review of current scientific literature on Alzheimer's disease genetics.
  • Analysis of recent research correlating somatic brain mutations with tauopathy progression.
  • Discussion of artificial intelligence and big data applications in neurodegenerative disease research.

Main Results:

  • Evidence suggests somatic mutations accumulate with aging and correlate with tauopathy progression in the brain.
  • Diverse genetic factors, beyond amyloid-β and tau, contribute to AD neurodegeneration.
  • AI and big data show promise for identifying causal somatic mutation biomarkers and accelerating drug discovery.

Conclusions:

  • Somatic mutations represent a significant, yet under-explored, factor in the pathogenesis of late-onset Alzheimer's disease.
  • Targeting somatic mutations offers a potential new avenue for AD therapeutic strategies.
  • AI and big data are crucial for advancing our understanding and treatment of AD and other neurodegenerative diseases.