A machine learning-enabled blood transcriptomic signature for digital diagnosis and subtyping of Alzheimer's disease.

Abstract

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.