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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Robel K Gebre1, Scott A Przybelski1, Sheelakumari Raghavan1

  • 1Mayo Clinic, Rochester, MN, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning score, THETA, effectively identifies cognitive resilience in Alzheimer's disease by detecting reduced tau pathology. This score aids in understanding resilience mechanisms and tracking disease progression.

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Area of Science:

  • Neuroscience
  • Biomarkers
  • Machine Learning

Background:

  • Distinguishing cognitive resilience from typical Alzheimer's disease (AD) tau pathology is challenging due to overlapping biomarker profiles.
  • Cognitive resilience to AD pathology is a critical area of research for understanding disease mechanisms.
  • Machine learning (ML) approaches offer potential for novel biomarker development in AD.

Purpose of the Study:

  • To evaluate the tau heterogeneity score (THETA) as an effective marker of cognitive resilience to Alzheimer's disease.
  • To assess THETA's ability to provide a better estimation of tau's temporal progression compared to existing biomarkers.
  • To differentiate between typical AD, resilient, and resistant groups based on tau distribution and progression.

Main Methods:

  • Longitudinal imaging data from the Mayo Clinic Study of Aging (MCSA) and ADRC were analyzed.
  • Participants were categorized into typical AD, resilient, and resistant groups using amyloid PiB-PET, Flortaucipir (FTP-PET), and MMSE.
  • Regional THETA scores were generated using an ML model to capture spatial tau heterogeneity; progression was modeled using SILA with plasma biomarkers (p-tau181, GFAP).

Main Results:

  • THETA identified regions of reduced tau uptake and preserved cortical thickness in resilient individuals compared to the typical AD group.
  • The Sampled Iterative Local Approximation (SILA) model demonstrated THETA's ability to distinguish global tau temporal progressions across different groups.
  • Earlier onset of GFAP (inflammation marker) was observed in resilient and typical AD groups, suggesting early inflammatory processes.

Conclusions:

  • THETA effectively identifies reduced tau pathology and preserved cortical thickness in resilient individuals, differentiating them from typical AD.
  • THETA shows potential in uncovering pathological tau mechanisms underlying resilience and enhancing understanding of AD.
  • The findings suggest THETA can provide valuable insights into resilience mechanisms and improve AD progression modeling.