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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

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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.
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...
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Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

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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...
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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.

Dishaa Sinha1, Kushagra Soni2, Ishan Durve3

  • 1University of Oxford, Oxford, Oxfordshire, United Kingdom.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary

Predicting Alzheimer's disease (AD) ATN pathologies using cardiovascular and speech data shows promise. Combining these non-invasive markers significantly improved diagnostic accuracy, offering a scalable approach for early detection.

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Computational Biology

Background:

  • Alzheimer's disease (AD) poses a growing healthcare challenge, necessitating early diagnosis for effective intervention.
  • Traditional AD diagnostics (neuroimaging, invasive biomarkers) are costly and inaccessible for routine monitoring.
  • Non-invasive, affordable biomarkers are crucial for early AD detection, aligning with the ATN (Amyloid, Tau, Neurodegeneration) framework.

Purpose of the Study:

  • To investigate the potential of speech characteristics and cardiovascular factors for predicting Alzheimer's disease (AD) ATN pathologies.
  • To develop and compare machine learning models for early AD detection using integrated non-invasive data.
  • To establish a scalable and accessible diagnostic approach for AD.

Main Methods:

  • Aggregated data from 1,011 participants using the Bio-Hermes platform.
  • Classified participants into ATN groups and clinical categories (cognitively healthy, MCI, AD) using biomarker thresholds.
  • Developed and evaluated four XGBoost models: Baseline, Cardiovascular, Speech, and Combined, incorporating demographic, lifestyle, cardiovascular, and speech features.

Main Results:

  • The Baseline model achieved an ROC-AUC of 0.9103 and accuracy of 0.7097.
  • The Cardiovascular and Speech models showed improved performance with ROC-AUCs of 0.9254 and 0.9416, respectively.
  • The Combined model integrating all features yielded the highest performance, with an ROC-AUC of 0.9539 and accuracy of 0.8260.

Conclusions:

  • Integrating cardiovascular and speech features significantly enhances the prediction of ATN pathologies in Alzheimer's disease.
  • These non-invasive markers demonstrate potential for scalable, cost-effective early AD detection.
  • The findings support the development of novel tools to reduce reliance on invasive diagnostic methods for AD.