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High-Performance Plasma Biomarker Panel for Alzheimer's Disease Screening Using a Femtomolar-Level Label-Free

Shicai Wang1,2, Ruijun Deng2, Zhiya Chen1,3

  • 1State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China.

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|December 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel plasma biomarker panel for early Alzheimer's disease (AD) diagnosis. The ultrasensitive sensor and machine learning approach accurately stage dementia progression, improving upon existing methods.

Keywords:
Alzheimer’s diseasegraphene field-effect transistormachine learningneurodegenerative diseaseplasma biomarkerplasma screening panel

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) is a leading cause of dementia, but current diagnostic methods like cerebrospinal fluid analysis or PET scans have limitations.
  • Noncognitive diagnostic approaches for AD are needed to improve accessibility and patient comfort.
  • Early and accurate diagnosis of AD is crucial for timely intervention and management.

Purpose of the Study:

  • To develop and validate an ultrasensitive plasma biomarker panel for the early detection and staging of Alzheimer's disease (AD).
  • To assess the performance of a machine-learning algorithm in combining plasma biomarkers for improved AD diagnosis.
  • To compare the diagnostic accuracy of the novel biomarker panel against established cognitive and imaging assessments.

Main Methods:

  • Utilized an ultrasensitive graphene field-effect transistor sensor to quantify femtomolar levels of AD biomarkers (Aβ40, Aβ42, P-tau181, P-tau217, NfL) in human plasma.
  • Employed a machine-learning algorithm to integrate these plasma biomarkers and clinical information into a "composite-info" panel.
  • Validated the panel's performance in multicenter clinical cohorts for discriminating AD dementia progression stages.

Main Results:

  • The "composite-info" biomarker panel achieved an area under the curve (AUC) of >0.94 in receiver operator characteristic (ROC) curve analysis for staging AD progression.
  • The panel demonstrated superior performance in individual-based stage assessment compared to the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and nuclear magnetic resonance imaging (MRI).
  • Ultrasensitive detection of key AD biomarkers in plasma was achieved, enabling femtomolar-level quantification.

Conclusions:

  • A novel composite biomarker panel, combining plasma Aβ40, Aβ42, P-tau181, P-tau217, NfL, and clinical data, offers a highly accurate method for AD screening and early diagnosis.
  • The developed rapid detection system and machine learning approach provide a non-invasive and effective tool for assessing Alzheimer's disease progression.
  • This study presents a significant advancement in developing accessible diagnostic tools for Alzheimer's disease, moving beyond invasive procedures.