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<p>Early and accessible detection of Alzheimer’s disease (AD) remains a major clinical challenge. We developed a machine learning–based blood transcriptomic model, the Lactylation-Derived Score (LDS), from lactylation-related genes across nine AD cohorts, using a standardized pipeline with z-score normalization, random forest–based feature screening, plsRglm modeling, and 10-fold cross-validation. LDS was externally tested in seven independent brain transcriptomic datasets and clinically validated in an independent plasma cohort (<i>n</i> = 540); logistic regression was used to integrate LDS with plasma phosphorylated tau 181 (p-tau181) and p-tau217. LDS achieved an AUC of 0.897 (95% CI 0.849–0.934) in the Training Cohort and 0.772 (95% CI 0.729–0.815) in the plasma validation cohort, while the three-marker model (LDS + p-tau181 + p-tau217) yielded the highest diagnostic performance (AUC 0.859, 95% CI 0.824–0.893). LDS alone effectively identified AT⁺ individuals (AUC 0.861, 95% CI 0.827–0.897), and a five-gene classifier derived from LDS genes stratified amnestic mild cognitive impairment with an AUC of 0.809 (95% CI 0.714–0.836). LDS-high individuals showed neuroinflammatory activation and metabolic stress signatures, indicating that this scalable, interpretable transcriptomic model complements plasma p-tau biomarkers and supports precision digital medicine in AD.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月5日

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

摘要

Early and accessible detection of Alzheimer’s disease (AD) remains a major clinical challenge. We developed a machine learning–based blood transcriptomic model, the Lactylation-Derived Score (LDS), from lactylation-related genes across nine AD cohorts, using a standardized pipeline with z-score normalization, random forest–based feature screening, plsRglm modeling, and 10-fold cross-validation. LDS was externally tested in seven independent brain transcriptomic datasets and clinically validated in an independent plasma cohort (n = 540); logistic regression was used to integrate LDS with plasma phosphorylated tau 181 (p-tau181) and p-tau217. LDS achieved an AUC of 0.897 (95% CI 0.849–0.934) in the Training Cohort and 0.772 (95% CI 0.729–0.815) in the plasma validation cohort, while the three-marker model (LDS + p-tau181 + p-tau217) yielded the highest diagnostic performance (AUC 0.859, 95% CI 0.824–0.893). LDS alone effectively identified AT⁺ individuals (AUC 0.861, 95% CI 0.827–0.897), and a five-gene classifier derived from LDS genes stratified amnestic mild cognitive impairment with an AUC of 0.809 (95% CI 0.714–0.836). LDS-high individuals showed neuroinflammatory activation and metabolic stress signatures, indicating that this scalable, interpretable transcriptomic model complements plasma p-tau biomarkers and supports precision digital medicine in AD.