Discovering non-linear dynamics of miRNAs in Alzheimer's disease-related cognitive impairment: a cross-species approach with explainable machine learning.

backgroundMicroRNA (miRNA) biomarker studies in Alzheimer's disease (AD) typically assume monotonic relationships between expression levels and disease status, overlooking the non-linear, context-dependent nature of miRNA regulatory networks. This simplification limits mechanistic insight and clinical translation. We aimed to characterise non-linear contribution patterns of miRNAs across the AD continuum using explainable machine learning and to define stage-specific "operating windows" where individual miRNAs drive classification.

methodsCandidate miRNAs were prioritised from APPtg/TAUtg mouse hippocampus (accession number GSE110743) using minimum-redundancy-maximum-relevance selection. A three-miRNA panel (miR-155-5p, miR-339-5p, and miR-455-5p) was validated in human serum (GSE120584; AD, MCI, and healthy controls). Linear and non-linear classifiers were compared, and SHAP dependence analysis was used to quantify sample-level contributions across expression ranges.

resultsNon-linear models (SVM-RBF and k-NN) consistently outperformed linear classifiers, with discrimination strongest for MCI vs. healthy controls (AUC: 0.844). SHAP analysis revealed that miR-155-5p functions as a stable primary driver across disease stages, whereas miR-339-5p and miR-455-5p act as context-dependent modulators contributing only within restricted expression ranges. Each miRNA exhibited distinct, stage-specific non-linear operating windows with threshold effects and inflection points rather than uniform dose-response patterns.

conclusionsThis study reframes circulating miRNAs as dynamic, interaction-governed signals rather than static biomarkers. The operating window framework provides interpretable, threshold-aware guidance for clinical decision-making and supports stage-sensitive early screening strategies.