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Parissa Fereydouni-Forouzandeh1,2, Nicolas Doyon1,2,3, Simon Duchesne1,2,3

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Summary
This summary is machine-generated.

This study enhances a mathematical model of brain health by incorporating metabolic changes, aiming for earlier Alzheimer's disease (AD) diagnosis. The improved model predicts metabolic trajectories linked to AD biomarkers and risk factors.

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Area of Science:

  • Neuroscience
  • Mathematical Biology
  • Metabolic Research

Background:

  • Uncertainty persists regarding Alzheimer's disease (AD) pathogenesis, with ongoing research into unifactorial theories.
  • A previously proposed multi-scale, multi-factorial causal framework for brain health utilized ordinary differential equations (ODEs).
  • The prior model did not account for the progressive downregulation of metabolic changes observed in AD.

Purpose of the Study:

  • To extend the existing brain health model by integrating a comprehensive metabolic component.
  • To incorporate diurnal constraints, energy balance, and regional brain metabolism into the model.
  • To develop a more holistic framework for understanding brain health and predicting AD progression.

Main Methods:

  • Extending a prior ODE-based brain health model with Göbel's brain-centered metabolic model.
  • Modifying the metabolic model to include 24-hour cycles, sleep phases, and energy balance dynamics.
  • Incorporating regional brain metabolism based on local cell concentrations and validating predictions against large-scale human cohort data (N>>3,500).

Main Results:

  • The original 12-hour model was replicated, with ongoing parameter validation from existing literature.
  • Ordinary differential equations (ODEs) are being adapted for a full 24-hour metabolic cycle.
  • Literature review is underway to establish diurnal concentration patterns for glucose, insulin, and ghrelin.

Conclusions:

  • A comprehensive lifespan brain health model incorporating metabolism can estimate metabolic trajectories preceding symptomatic AD.
  • The model aims to predict future AD biomarker trajectories based on individual risk profiles.
  • This approach could facilitate earlier diagnosis of Alzheimer's disease.