アルツハイマー病のデジタル診断とサブタイピングのための機械学習対応血液トランスクリプトームシグネチャー

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.