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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Gloria Chiang1, Seyed Hani Hojjati1, Bardiya Ghaderi Yazdi1

  • 1Weill Cornell Medicine, New York City, NY, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

A new imaging index accurately predicts future tau accumulation in Alzheimer's Disease (AD). This tool combines amyloid (Aβ) and tau imaging with subject-specific brain connectivity, outperforming conventional methods for better AD prognosis.

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Area of Science:

  • Neuroimaging
  • Alzheimer's Disease Research
  • Biomarker Discovery

Background:

  • Alzheimer's Disease (AD) prognosis is highly variable, complicating treatment and patient counseling.
  • Similar levels of amyloid (Aβ) and tau deposition do not predict disease progression uniformly.
  • Challenges exist in developing targeted therapies and providing accurate prognosis for AD patients.

Purpose of the Study:

  • To develop and validate a predictive imaging index for future tau accumulation in Alzheimer's Disease.
  • To assess the efficacy of incorporating subject-specific connectivity into Alzheimer's Disease prognostication.
  • To improve the accuracy of Alzheimer's Disease progression prediction beyond global Aβ and tau burden.

Main Methods:

  • Developed a Tau Pathology Index (TPI) integrating remote and local Aβ-tau interactions, and subject-specific functional and structural connectivity.
  • Utilized a LASSO model with TPI components to predict longitudinal tau accumulation in early AD participants.
  • Compared prediction accuracy of TPI derived from subject-specific connectomes versus group-averaged connectomes.

Main Results:

  • The subject-specific TPI significantly outperformed group-averaged TPI in predicting longitudinal tau accumulation (p < 0.009).
  • The TPI model explained 30% more variance in future tau accumulation compared to the conventional model.
  • Both TPI models showed significant prediction of tau accumulation, with subject-specific connectivity yielding stronger results (r=0.8).

Conclusions:

  • An imaging index combining baseline Aβ, tau, and subject-specific connectivity accurately predicts future tau accumulation in AD.
  • This novel index offers a promising tool for improving Alzheimer's Disease prognosis.
  • Further validation in larger cohorts is underway to confirm these findings.