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.
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Data availability
The public transcriptomic datasets used in this study were obtained from the Gene Expression Omnibus (GEO) database under accession numbers GSE5281, GSE84422, GSE122063, GSE132903, GSE28146, GSE48350, GSE36980, GSE37263, and GSE29378. These datasets are freely available at “https://www.ncbi.nlm.nih.gov/geo/. The independent plasma cohort data generated and analyzed during this study are not publicly available due to ethical and privacy restrictions but are available from the corresponding author upon reasonable request and with appropriate institutional approval.
Code availability
The analytical codes involved in this study can be obtained by contacting the corresponding author upon reasonable request.
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Acknowledgements
This work was Supported by the National Natural Science Foundation of China (82373781), Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit (JSDW202240), the Jiangsu Provincial Key Laboratory of Critical Care Medicine (JSKLCCM202202015), Southeast University Doctoral Students Innovation Ability Enhancement Program (CXJH_SEU_24219), Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Pairing Assistance Construction Funds (zdlyg09) and Health Research project of Health Commission of Jiangsu Province (BJ23014).
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Conception or design: S.M., D.W.C., Y.H.C., G.Q.W. Acquisition, analysis, or interpretation of data: S.M., D.W.C., Y.H.C., G.Q.W. Sample collection and data processing: Y.Z.L., M.L.Z., J.W.W., Y.M.Y., Y.X.L. Drafting the work or revising: S.M., Y.H.C. Final approval of the manuscript: all authors.
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Ma, S., Chen, D., Li, Y. et al. A machine learning–enabled blood transcriptomic signature for digital diagnosis and subtyping of Alzheimer’s disease. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-025-02305-7
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DOI: https://doi.org/10.1038/s41746-025-02305-7
