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Updated: Jan 7, 2026

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

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

Published on: January 28, 2014

Biomarkers.

Akira A Nair¹, Zixuan Wen¹, Zexuan Wang¹

¹University of Pennsylvania, Philadelphia, PA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association

|December 24, 2025

Summary

This summary is machine-generated.

Alzheimer's disease progression can be staged using amyloid-beta plaques, neurofibrillary tangles, and neuronal loss biomarkers. Computational methods like PHATE, Slingshot, and SuStaIn reveal distinct but converging trajectories for these biomarkers, aiding in disease prediction.

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Area of Science:

Neuroscience
Biomarker Discovery
Computational Biology

Background:

Alzheimer's disease (AD) is characterized by amyloid-beta plaques (A), neurofibrillary tangles (T), and neuronal loss (N), collectively known as A/T/N.
Understanding the spatial progression of neurodegeneration is crucial for predicting AD trajectories and outcomes.

Purpose of the Study:

To explore the staging and pseudotime of AD patients using A/T/N biomarkers.
To utilize Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Main Methods:

Applied PHATE for dimensionality reduction to visualize disease progression trajectories for A/T/N modalities.
Used Slingshot to translate PHATE embeddings into 1D pseudotime values.
Employed SuStaIn, a machine learning algorithm, to predict patient stages and biomarker sequences.
Compared SuStaIn stage predictions with PHATE/Slingshot pseudotime values to assess robustness.

Main Results:

SuStaIn predicted stages closely aligned with PHATE/Slingshot pseudotime values across all modalities.
Amyloid PET and tau PET showed stronger trajectory alignments than MRI-based volume.
Biomarker event sequences from SuStaIn agreed with PHATE pseudotime-informed models.
Amyloid and tau modalities exhibited moderate pseudotime correlation but distinct driving biomarker events.

Conclusions:

Integrative analysis of computational methods enhances confidence in disease progression timing.
Amyloid and tau biomarkers appear to follow distinct cortical trajectories.
Different computational approaches independently yielded similar findings regarding disease progression.