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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

749
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
749
Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

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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.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
516

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Updated: Jan 7, 2026

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

Roger A Dixon1, Shannon M Drouin1, Linzy Bohn1

  • 1University of Alberta, Edmonton, AB, Canada.

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

New molecular biomarkers from blood and saliva significantly improve Alzheimer's disease prediction when combined with existing risk factors. This integration enhances early detection and highlights the potential of non-invasive salivary diagnostics.

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Machine Learning

Background:

  • Alzheimer's disease (AD) research increasingly utilizes large datasets to identify predictive biomarkers and risk factors.
  • Analytical techniques can evaluate numerous candidate factors in high-dimensional data to pinpoint leading predictors.
  • Integrating novel molecular biomarkers with established AD indicators is crucial for comprehensive predictive modeling.

Purpose of the Study:

  • To integrate novel molecular biomarkers with established Alzheimer's disease (AD) biomarkers and risk factors.
  • To evaluate the relative predictive contributions of these integrated factors using machine learning classifiers.
  • To identify key predictors for distinguishing between cognitively unimpaired, mild cognitive impairment, and AD states.

Main Methods:

  • Assembled 92 AD-related indicators across 10 modalities from the COMPASS-ND database (CU, MCI, AD cohorts).
  • Conducted high-throughput metabolomics on serum and saliva, identifying over 7000 metabolite peaks.
  • Utilized machine learning classifiers (e.g., RandomForestClassifier) with 112 predictors (92 established + 20 novel).

Main Results:

  • All models demonstrated high performance (Accuracy, Precision, AUC 0.81-0.98) in predicting AD, MCI, and dementia.
  • Explainable AI (Tree SHAP) plots revealed discriminating patterns of multi-modal predictors.
  • Top predictors included novel metabolites (8/20), particularly from saliva (5/8), alongside imaging, vascular, and established AD biomarkers.

Conclusions:

  • Novel molecular biomarkers from blood and saliva show strong predictive performance when integrated with established AD data.
  • Findings support enhancing early mechanistic pathway identification and the value of integrative, data-driven biomarker analyses.
  • Non-invasive salivary biomarkers offer potential for increased accessibility in Alzheimer's disease assessment.