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

Sebastian Moguilner1,2, Sandra Baez3,4, Hernan Hernandez5

  • 1Massachusetts General Hospital, Boston, MA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

Brain aging and dementia diagnostics are limited by data from homogeneous populations. This study reveals socioeconomic and environmental factors significantly impact brain-age gaps, particularly in diverse global regions, offering scalable solutions.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Global Health

Background:

  • Current brain aging and dementia diagnostics rely on data from the Global North, limiting applicability to diverse populations.
  • Significant gaps exist in understanding how geographical, socioeconomic, and demographic factors influence accelerated brain aging globally, especially in underserved regions.

Purpose of the Study:

  • To develop and apply advanced computational methods to assess brain aging across diverse global populations.
  • To investigate the impact of exposomal factors on brain-age gaps (BAGs) and identify disparities in brain aging.

Main Methods:

  • Developed a DenseNet classifier for structural MRI analysis in dementia patients and controls.
  • Utilized a graph-based deep learning architecture to analyze brain-age gaps (BAGs) from fMRI and EEG data across 15 countries.
  • Employed nonlinear Generalized Additive Models (GAMs) to associate BAGs with exposomal factors in a multimodal approach.

Main Results:

  • The DenseNet classifier showed robust performance on clinical MRI data, identifying disease-specific patterns.
  • Significantly older brain ages were observed in Latin American and Caribbean (LAC) populations, linked to socioeconomic inequality, pollution, and health disparities.
  • Generalized Additive Models revealed significant associations between BAGs and factors like Human Development Index, economic equality, and air pollution (p < 0.0001).

Conclusions:

  • Findings offer scalable solutions for accelerated brain aging disparities, especially in underserved areas.
  • Multimodal meta-models using GAMs enhance the accuracy of BAGs estimations.
  • Personalized AI tools can advance the study of accelerated brain aging trajectories and modifiable risk factors.