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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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Updated: Jan 7, 2026

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

Bin Jiao1, Ziyu Ouyang1, Shilin Luo1

  • 1Xiangya Hospital, Central South University, Changsha, Hunan, China.

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

Machine learning digital biomarkers from plasma spectra offer a low-cost method for early Alzheimer's disease (AD) detection and differentiation from other neurodegenerative diseases. This approach enhances diagnostic accessibility and accuracy in clinical settings.

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Area of Science:

  • Biomarkers and Diagnostics
  • Machine Learning in Medicine
  • Neurodegenerative Diseases

Background:

  • Alzheimer's disease (AD) diagnosis relies on complex biological pathway alterations, necessitating effective blood biomarkers.
  • Current blood biomarker methods face limitations in cost-effectiveness and operational complexity, hindering widespread clinical adoption.

Purpose of the Study:

  • To develop low-cost, convenient machine learning-based digital biomarkers (MLDB) using plasma spectra.
  • To detect AD or mild cognitive impairment (MCI) in healthy controls (HCs).
  • To discriminate AD from other neurodegenerative diseases like Lewy body dementia (DLB), frontotemporal dementia (FTD), and progressive supranuclear palsy (PSP).

Main Methods:

  • Utilized Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) plasma spectra data from 1,324 individuals.
  • Employed random forest classifiers and feature selection to identify digital biomarkers.
  • Included participants with AD, MCI, DLB, FTD, PSP, and HCs in the study cohort.

Main Results:

  • MLDB achieved high diagnostic accuracy: AUC of 0.92 for AD vs HC and 0.89 for MCI vs HC.
  • Demonstrated ability to discriminate AD from other neurodegenerative diseases with AUCs ranging from 0.80 to 0.93.
  • Identified digital biomarkers negatively correlated with plasma p-tau217 and GFAP levels.

Conclusions:

  • ATR-FTIR plasma spectral features can identify AD-related pathological changes.
  • These spectral features function as effective digital biomarkers for AD.
  • The developed MLDB approach significantly aids in early screening and diagnosis of AD, improving accessibility.