Breakthrough in Alzheimer's Disease Research: Transfer Learning Reveals Mediating Mechanisms

Strategic Insights -

Researchers at Fudan University in Shanghai, People's Republic of China, have made a significant discovery in understanding the mechanisms behind Alzheimer's disease. By leveraging transfer learning, a high-dimensional mediation analysis model was developed to identify potential mediators in small sample target data. This approach improved the power in identifying true mediator variables while effectively controlling the family-wise error rate in multiple testing.

Key Takeaways:

  • The research developed a high-dimensional mediation analysis model (TransHDM) based on a transfer learning framework to identify potential mediators in small sample target data.
  • The TransHDM model 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.
  • The method significantly enhances the ability to identify potential mediators in small sample target data, especially in minority populations with limited sample sizes.
  • 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.
  • Additional lipid metabolic pathways, including glycerophospholipid metabolism, glycerolipid metabolism, sphingolipid metabolism, and ether lipid metabolism, were identified as mediating the influence of the APOE epsilon 4 allele on AD pathological progression in African American populations.
  • The study obtained significant results from the Alzheimer's Disease Neuroimaging Initiative cohort, demonstrating the effectiveness of the TransHDM approach in identifying potential mediators.
  • The research was supported by the National Natural Science Foundation of China, Shanghai Rising-Star Program, Shanghai Municipal Natural Science Foundation, Three-Year Public Health Action Plan of Shanghai, Shanghai Talent Programs, and Shanghai Municipal Science and Technology Major Project.

Statistics:

  • The research involved 12 authors from Fudan University and other institutions.
  • The study used transfer learning to analyze aggregated data from the Alzheimer's Disease Neuroimaging Initiative cohort.
  • The family-wise error rate was effectively controlled in multiple testing using the TransHDM approach.
  • The false discovery rate (FDR) for the identified lipid metabolic pathways was reported as supplementary information.
  • The research was published in Briefings in Bioinformatics, 2025;26(5).

Sources:

  • NewsRx. Reports from Fudan University Add New Data to Findings in Alzheimer Disease (Transfer Learning Reveals the Mediating Mechanisms of Cross-ethnic Lipid Metabolic Pathways In the Association Between Apoe Gene and Alzheimer's Disease). Health & Medicine Week. October 24, 2025; p 4608.
  • Briefings in Bioinformatics. Transfer Learning Reveals the Mediating Mechanisms of Cross-ethnic Lipid Metabolic Pathways In the Association Between Apoe Gene and Alzheimer's Disease. 2025;26(5).