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A proposed theoretical framework for retinal biomarkers.

Ian James Callum MacCormick1, Bo Zhang2, Daniel Hill1,3

  • 1Centre for Inflammation Research University of Edinburgh The Queen's Medical Research Institute Edinburgh BioQuarter Edinburgh UK.

Alzheimer'S & Dementia (Amsterdam, Netherlands)
|June 30, 2022
PubMed
Summary

Developing retinal biomarkers for Alzheimer's disease (AD) requires a new theoretical framework. This Bayesian network approach clarifies assumptions, aiding biomarker discovery and validation for clinical use.

Keywords:
Alzheimer's diseaseBayesianbiomarkerbrain diseasecerebral malariaretinasurrogatevalidation

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Area of Science:

  • Neuroscience
  • Ophthalmology
  • Biomarker Discovery

Background:

  • The retina and brain share biological features relevant to Alzheimer's disease (AD).
  • Developing validated clinical retinal biomarkers for AD remains a priority but faces challenges.
  • Overlooked inferential assumptions may hinder biomarker discovery and validation.

Purpose of the Study:

  • Propose a theoretical framework for retinal biomarkers of Alzheimer's disease (AD).
  • Incorporate fundamental inferential assumptions into biomarker discovery and validation processes.
  • Facilitate the translation of retinal biomarkers for clinical use in AD.

Main Methods:

  • Utilize a Bayesian network to express the biological assumption of analogous effects in the brain and retina.
  • Employ graphical representation to visualize relationships between variables and assess assumptions.
  • Adapt the framework to accommodate alternative theories and confounding factors.

Main Results:

  • The proposed framework offers a visual approach to understanding retinal biomarkers for AD.
  • It provides a means to falsify the retina-brain analogy hypothesis.
  • The framework can rationalize analysis of existing and future studies, potentially explaining inconsistent results.

Conclusions:

  • The theoretical framework enhances the assessment of how well retinal biomarkers reflect brain pathology in AD.
  • It aids in identifying and ruling out poor biomarker candidates using data from both eyes.
  • Incorporating this framework can facilitate the development and clinical validation of novel AD biomarkers.