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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.
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Cardiac myocytes produce these hormones in response to ventricular stretching...
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Stephanie Yiallourou1, Tiffany F Kautz2, Julie Parker-Garza3

  • 1School of Psychological Sciences & Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia.

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

Plasma biomarkers like pTau217 show promise in differentiating Alzheimer's disease (AD) and frontotemporal dementia (FTD). Machine learning models using these markers aid early diagnosis, though further refinement is needed for Lewy body/Parkinson's disease dementia.

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Area of Science:

  • Neuroscience
  • Biomarker Research
  • Machine Learning in Medicine

Background:

  • Plasma biomarkers are effective for Alzheimer's disease (AD) detection but underexplored for differentiating dementia subtypes.
  • Early differential diagnosis of AD, frontotemporal dementia (FTD), and Lewy body/Parkinson's disease (LBD/PD) dementia is challenging.
  • This study evaluates plasma biomarkers for distinguishing these early-stage dementia subtypes using machine learning.

Purpose of the Study:

  • To assess the efficacy of plasma biomarkers in differentiating between AD, FTD, and LBD/PD dementia.
  • To employ machine learning algorithms for enhanced diagnostic accuracy.
  • To identify key plasma biomarkers contributing to differential dementia diagnosis.

Main Methods:

  • Recruited patients with early AD, FTD, and LBD/PD dementia.
  • Analyzed plasma for Aβ40, Aβ42, pTau181, pTau217, TDP-43, NfL, GFAP, and YKL40 using Simoa and MSD platforms.
  • Developed dementia subtype classification models using the SuperLearner algorithm, incorporating age, sex, and all plasma biomarkers, assessed by AUC.

Main Results:

  • Machine learning models combining all biomarkers achieved excellent discrimination for AD (AUC=0.84) and FTD (AUC=0.85) from other dementias.
  • Moderate discrimination was observed for LBD/PD dementia (AUC=0.72).
  • pTau217 was the strongest individual predictor for AD differentiation; NfL showed the highest contribution for FTD, driven by a combination of markers.

Conclusions:

  • pTau217 is a key plasma biomarker for differentiating AD.
  • FTD classification relies on a combination of biomarkers, with NfL showing the highest individual contribution.
  • Plasma biomarkers show significant potential for early, non-invasive differential diagnosis of AD and FTD, with further optimization needed for LBD/PD.