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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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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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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.

Jingjing Zhu1, Ben Dai2, Unhee Lim3

  • 1University of Hawai'i Manoa, Honolulu, HI, USA.

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

This study reveals novel protein biomarkers linked to Alzheimer's disease (AD) risk by exploring nonlinear associations. Our findings enhance understanding of AD pathogenesis and may guide future therapeutic strategies.

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Area of Science:

  • Neuroscience
  • Genetics
  • Biomarker Discovery

Background:

  • Alzheimer's disease (AD) presents a major health challenge with limited effective treatments.
  • Understanding systemic physiological factors is key to developing novel AD therapies.
  • Previous studies overlooked nonlinear associations between protein biomarkers and AD risk.

Purpose of the Study:

  • To investigate nonlinear associations between genetically predicted protein concentrations and AD risk.
  • To identify novel protein biomarkers for AD using advanced statistical modeling.

Main Methods:

  • Employed a nonlinear modeling approach: two-stage sliced inverse regression (2SIR) with adjusted inverse regression (AIR).
  • Integrated proteome and genome data from the INTERVAL study with AD genome-wide association study summary statistics.

Main Results:

  • Identified 131 proteins associated with AD risk after Bonferroni correction.
  • 46 proteins were newly identified using the nonlinear approach, complementing linear methods.
  • Key AD-associated proteins like APOE, ADAM11, LRP1B, and TREML2 were identified, highlighting the importance of nonlinear analysis.

Conclusions:

  • Accounting for nonlinear relationships is crucial for discovering AD-associated genes and proteins.
  • This nonlinear method can advance the understanding of AD pathogenesis.
  • Findings may inform future therapeutic and preventive strategies for AD.