ORIGINAL RESEARCH article

Front. Aging Neurosci.

Sec. Alzheimer's Disease and Related Dementias

Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1637368

miR-423-5p and miR-92a-3p in Alzheimer's disease: relationship with pathology and cognition

Provisionally accepted
Sang-Won  HanSang-Won Han1,2Young Ho  ParkYoung Ho Park3,4*Jung-Min  PyunJung-Min Pyun3Paula  J. BicePaula J. Bice5SangYun  KimSangYun Kim2,3Andrew  J. SaykinAndrew J. Saykin5Kwangsik  NhoKwangsik Nho5*
  • 1Soonchunhyang University Hospital, Yongsan-gu, Republic of Korea
  • 2Soonchunhyang University College of Medicine, Cheonan-si, Republic of Korea
  • 3Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
  • 4Seoul National University College of Medicine, Jongno-gu, Republic of Korea
  • 5Indiana University School of Medicine, Indianapolis, United States

The final, formatted version of the article will be published soon.

Background: MicroRNAs (miRNAs), small and highly conserved non-coding RNA molecules, have emerged as promising molecular biomarkers due to their regulatory roles in gene expression and stability in blood. Methods: We used measurements of 64 plasma miRNAs from 145 participants in the Alzheimer's Disease Neuroimaging Initiative cohort, including 74 probable AD patients and 71 cognitively normal (CN) older adults. We performed principal component analysis (PCA) with factor rotation for dimension reduction to identify AD-associated principal components (PCs) and their key miRNAs with factor loadings higher than 0.8. We investigated their association with amyloid/tau/neurodegeneration (A/T/N) biomarkers and cognition. After identifying the candidate target genes of key miRNAs, we performed pathway enrichment analysis. We conducted mediation analyses to assess the effect of the associations between miRNAs and A/T/N biomarkers on AD diagnosis and cognition. Finally, we used a machine learning approach to evaluate the performance of key miRNAs for AD classification. Results: PCA identified one PC as significantly associated with AD. The PC was also significantly associated with CSF p-tau levels, hippocampal volume, and cognition. Two key miRNAs (miR-423-5p and miR-92a-3p) in the PC were associated with AD. Lower levels of miR-423-5p and miR-92a-3p were associated with reduced hippocampal volume and worse cognition, and lower levels of miR-423-5p were associated with higher brain amyloid deposition. Pathway enrichment analysis identified several significant biological processes, including memory, protein phosphorylation, and the phosphatidylinositol-3-phosphate biosynthetic process. Mediation analysis revealed that miR-423-5p, but not miR-92a-3p, had indirect effects on AD diagnosis and memory performance through brain amyloid deposition and brain atrophy. Machine learning analysis demonstrated that incorporating two key miRNAs improved the performance of demographic information for AD classification. Conclusions: Plasma miR-423-5p and miR-92a-3p are implicated in AD pathology and cognitive decline, providing insights into their roles in disease mechanisms. This study suggests the potential of these miRNAs as blood-based molecular biomarkers for AD.

Keywords: Alzheimer's disease, cognitive decline, machine learning, MicroRNAs, Mir-423-5p, miR-92a-3p

Received: 29 May 2025; Accepted: 08 Jul 2025.

Copyright: © 2025 Han, Park, Pyun, Bice, Kim, Saykin and Nho. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Young Ho Park, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
Kwangsik Nho, Indiana University School of Medicine, Indianapolis, United States

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