A machine learning–enabled blood transcriptomic signature for digital diagnosis and subtyping of Alzheimer’s disease - Takeaways - MDSpire

A machine learning–enabled blood transcriptomic signature for digital diagnosis and subtyping of Alzheimer’s disease

  • By

  • Shuo Ma

  • Dawen Chen

  • Yanzhi Li

  • Yanxia Liu

  • Meiling Zhou

  • Jiwei Wang

  • Yuming Yao

  • Yinhao Chen

  • Guoqiu Wu

  • January 5, 2026

  • 0 min

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  • 1

    Alzheimer’s disease (AD) is a leading cause of dementia, characterized by cognitive decline and behavioral changes, significantly impacting quality of life.

  • 2

    Current diagnostic methods for AD, including CSF biomarkers, are limited by invasiveness and cost, highlighting the need for non-invasive biomarkers.

  • 3

    The study developed a Lactylation-Derived Score (LDS) using machine learning, which demonstrated high diagnostic performance for AD detection.

  • 4

    LDS scores correlated with disease progression indicators, showing potential for early detection and patient stratification in AD.

  • 5

    Transcriptomic analysis revealed lactylation dysregulation in AD, linking metabolic and epigenetic alterations to disease onset and progression.

Original Source(s)

A machine learning–enabled blood transcriptomic signature for digital diagnosis and subtyping of Alzheimer’s disease

  • by Shuo Ma, Dawen Chen, Yanzhi Li, Yanxia Liu, Meiling Zhou, Jiwei Wang, Yuming Yao, Yinhao Chen, Guoqiu Wu

  • January 5, 2026

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