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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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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.

Kay Hoong Chow1, Tongrong Wang2, Ilke Tunali3

  • 1Eli Lilly and Company, Bracknell, Berkshire, United Kingdom.

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

This study models amyloid plaque accumulation over decades, revealing age and APOE4 genotype as key factors. Understanding this progression aids in Alzheimer's disease (AD) prevention strategies and clinical trial design.

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Gerontology

Background:

  • Amyloid plaque accumulation is a hallmark of Alzheimer's disease (AD), potentially initiating preclinical stages.
  • Understanding the natural rate of amyloid accumulation is crucial for developing effective AD prevention strategies.
  • Previous studies suggest amyloid plaque accumulation may follow an exponential growth model influenced by various factors.

Purpose of the Study:

  • To establish a long-term (10+ years) amyloid plaque accumulation model using natural history data.
  • To identify significant factors influencing amyloid plaque progression across diverse cohorts.
  • To inform the design and interpretation of AD prevention trials.

Main Methods:

  • Utilized non-linear mixed-effects modeling with stepwise covariate selection and artificial intelligence.
  • Analyzed data from large cohorts: ADNI (N=1745), BioFINDER (N=265), and LEARN (N=4492).
  • Evaluated linear, exponential, and non-linear models to estimate accumulation rates and variability.

Main Results:

  • Identified age, baseline amyloid plaque levels, and APOE4 genotype as significant predictors of plaque accumulation.
  • Observed a steep increase in amyloid plaque around age 60, followed by saturation at older ages.
  • Noted that these changes initiate earlier in APOE4 carriers.

Conclusions:

  • Developed a predictive model for amyloid plaque accumulation over decades, applicable to AD clinical trial design.
  • Emphasized that preventing amyloid accumulation could avert preclinical AD and subsequent cognitive decline.
  • Highlighted the model's utility in interpreting novel AD therapeutic interventions targeting amyloid.