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Related Concept Videos

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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Related Experiment Video

Updated: Jan 7, 2026

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

Julie K Wisch1, Nicole S McKay2, Matthew D Zammit3

  • 1Washington University in St. Louis School of Medicine, St. Louis, MO, USA.

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

The Sampled Iterative Local Approximation (SILA) algorithm accurately estimates time since amyloid positivity in Autosomal Dominant Alzheimer Disease (ADAD). This Alzheimer disease (AD) research enables better disease staging and comparison across AD forms.

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Area of Science:

  • Neurology
  • Biomedical Imaging
  • Alzheimer Disease Research

Background:

  • Alzheimer Disease (AD) pathology progresses over decades, necessitating accurate staging for therapeutic timing.
  • Autosomal Dominant AD (ADAD) allows symptom onset prediction via estimated years to symptom onset (EYO), but this method lacks broad applicability.
  • A validated, broadly applicable timecourse for ADAD is crucial for understanding disease progression and intervention strategies.

Purpose of the Study:

  • Validate the Sampled Iterative Local Approximation (SILA) algorithm using a cohort with a known disease timecourse.
  • Establish a reliable method for estimating time from amyloid positivity (Atime) in Autosomal Dominant AD (ADAD) using longitudinal PET data.

Main Methods:

  • Evaluated Atime in a longitudinal ADAD cohort (N=316) using Positron Emission Tomography (PET) PiB data.
  • Compared SILA-predicted age at amyloid positivity (A+) with observed age at A+ for newly positive individuals.
  • Utilized linear regression and generalized additive models to compare Atime and EYO in relation to symptom onset and cognitive performance.

Main Results:

  • SILA demonstrated a mean average error of 1.15 years in predicting age at amyloid positivity.
  • SILA-estimated age at A+ explained 39% of the variance in estimated age at symptom onset (β=0.918, p<0.0001).
  • Atime explained 19% of cognitive variance, while EYO explained 43%, indicating a nonlinear association between cognition and disease progression markers.

Conclusions:

  • The SILA algorithm provides a valid estimation of time from amyloid positivity in ADAD.
  • This validation enables direct comparison of ADAD staging with other AD forms, overcoming limitations of EYO.
  • Significant heterogeneity in preclinical AD duration exists beyond amyloid status alone, highlighting the complexity of AD progression.