miR-423-5p and miR-92a-3p in Alzheimer’s disease: relationship with pathology and cognition
Bibliographic record
Abstract
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. Conclusion: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".