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Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

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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.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach.

Raghav Tandon1,2, Yajun Mei3, James J Lah4

  • 1Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA.

International Journal of Molecular Sciences
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an anatomical staging framework to predict Alzheimer's disease (AD) progression from mild cognitive impairment (MCI). The model identifies distinct subtypes and stages, improving prognostication for personalized treatments.

Keywords:
Alzheimer’s disease progressionagingartificial intelligencebehavioral neurologycognitive decline predictiondisease heterogeneitymild cognitive impairmentneurodegeneration

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Gerontology

Background:

  • Alzheimer's disease (AD) exhibits significant heterogeneity in clinical presentation and progression.
  • Predicting the transition from mild cognitive impairment (MCI) to AD is crucial for timely intervention and clinical trial design.

Purpose of the Study:

  • To develop and validate a comprehensive anatomical staging framework for predicting AD progression from MCI.
  • To identify distinct AD subtypes with differential progression rates and biomarker profiles.

Main Methods:

  • Applied the scalable Subtype and Stage Inference (s-SuStaIn) model to 118 neuroanatomical features from the ADNI database.
  • Validated the framework on MCI participants, assessing associations with clinical progression, CSF and FDG-PET biomarkers, and neuropsychiatric measures.
  • Adjusted for confounders including age, gender, education, and APOE ε4 status.

Main Results:

  • The anatomical staging framework demonstrated superior prognostic accuracy (C-index = 0.73) compared to traditional risk assessment (C-index = 0.62).
  • Identified four distinct AD subtypes with unique progression patterns, biomarker signatures (FDG-PET, CSF Aβ42), and cognitive trajectories.
  • Revealed stage-dependent cognitive deterioration, particularly affecting learning, visuospatial processing, and functional abilities.

Conclusions:

  • The data-driven framework effectively captures AD heterogeneity and enhances prognostication in MCI.
  • This approach holds potential for developing personalized therapeutic strategies and optimizing clinical trial design for Alzheimer's disease.