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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.
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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.

Tanya Talkar1, Daniel J Schulman1, David J Libon2,3,4

  • 1Linus Health, Boston, MA, USA.

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

The remote Digital Assessment of Cognition (rDAC) effectively identifies cognitive impairment (CI) using iPad-based tests. This tool shows promise for early detection of conditions like mild cognitive impairment (MCI) and dementia.

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Area of Science:

  • Neuroscience and Cognitive Science
  • Digital Health and Medical Technology
  • Gerontology and Aging Research

Background:

  • Current diagnostic criteria for mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) necessitate documented cognitive impairment (CI).
  • Traditional neuropsychological testing, while rigorous, presents a significant time barrier, potentially delaying early CI detection and access to treatments.
  • This study explores the utility of a novel remote Digital Assessment of Cognition (rDAC) for identifying CI.

Purpose of the Study:

  • To evaluate the effectiveness of the rDAC in distinguishing between cognitively unimpaired (CU) individuals, those with MCI, and those with dementia.
  • To assess the diagnostic performance of traditional, process, and acoustic metrics extracted automatically from the rDAC.
  • To determine if rDAC can serve as a viable, accessible tool for early cognitive impairment detection.

Main Methods:

  • The rDAC, administered via iPad, included verbal learning tests, semantic fluency, and digit span tests.
  • Participants were categorized as cognitively unimpaired (CU, n=24), MCI (n=42), or dementia (n=15) based on comprehensive assessments.
  • Random Forest models were trained to classify participants into different cognitive status groups (e.g., CU vs. MCI, CU vs. Dementia).

Main Results:

  • High accuracy was achieved in discriminating between CU and MCI (AUC=0.95, accuracy=0.87).
  • Excellent performance was observed in detecting any CI (CU vs. MCI+Dementia) with AUC=0.98 and accuracy=0.91.
  • Key discriminating features included speech articulation rates, pause duration, and acoustic speech properties (jitter, duration).

Conclusions:

  • The brief, iPad-based rDAC demonstrates significant potential as a sensitive tool for flagging early cognitive impairment.
  • rDAC performance is comparable to traditional, time-intensive neuropsychological testing methods.
  • The integration of outcome, process, and acoustic metrics in rDAC effectively captures subtle performance differences indicative of CI.