Lipid-mediated effects play a crucial role in elucidating the pathological mechanisms linking the ε4 allele of the apolipoprotein E gene (APOE ε4) to Alzheimer's disease (AD). However, traditional mediation analysis methods often suffer from insufficient statistical power in studies involving minority populations due to limited sample sizes. This study innovatively develops a high-dimensional mediation analysis model (TransHDM) based on a transfer learning framework. By leveraging information from source data with large-scale samples, it significantly enhances the ability to identify potential mediators in small sample target data. The method first constructs a high-dimensional regression model using aggregated data from the source data and target data, then applies transfer regularization to adjust for heterogeneity between the source and target domains, correcting for estimation bias in high-dimensional Lasso. Ultimately, it achieves parameter transfer across domains, addressing statistical bias and inferential uncertainty caused by small sample sizes. Simulation results demonstrate that, compared to traditional methods, this approach significantly improves the power in identifying true mediator variables while effectively controlling the family-wise error rate in multiple testing. When applied to the Alzheimer's Disease Neuroimaging Initiative cohort, TransHDM transferred large-scale data from white and other ethnic groups, identifying additional lipid metabolic pathways mediating the influence of the APOE ε4 allele on AD pathological progression in African American populations compared to pre-transfer analysis. These pathways include glycerophospholipid metabolism, glycerolipid metabolism, sphingolipid metabolism, and ether lipid metabolism (false discovery rate < 0.05). The TransHDM framework not only provides a powerful methodological tool for small sample population research but also offers valuable insights for future research in exploring disease mechanisms and developing biomarkers for disease prediction.

MOTIVATION: In ultra-high dimensional mediation analysis, confounding variables can influence both mediators and outcomes through complex functional forms. While machine learning (ML) approaches are effective at modeling such complex relationships, they can introduce bias when estimating mediation effects. In this article, we propose a debiased ML framework that mitigates this bias, enabling accurate identification of key mediators and precise estimation and inference of their respective contributions. RESULTS: We construct an orthogonalized score function and use cross-fitting to reduce bias introduced by ML. To tackle ultra-high dimensional potential mediators, we implement screening and regularization techniques for variable selection and effect estimation. For statistical inference of the mediators' contributions, we use an adjusted Sobel-type test. Simulation results demonstrate the superior performance of the proposed method in handling complex confounding. Applying this method to Alzheimer's Disease Neuroimaging Initiative data, we identify several cytosine-phosphate-guanine sites where DNA methylation mediates the effect of body mass index on Alzheimer's Disease. AVAILABILITY AND IMPLEMENTATION: The R function DML_HDMA implementing the proposed methods is available online at https://github.com/Wei-Kecheng/DML_HDMA.