A multi-omics approach identifies a blood-based miRNA signature of cognitive decline in two large observational trials
Bibliographic record
Abstract
Abstract Identifying individuals before the onset of overt symptoms is a key prerequisite for the prevention of Alzheimer’s disease (AD). A wealth of data reports dysregulated microRNA (miRNA) expression in the blood of individuals with AD, but evidence in individuals at subclinical stages is sparse. In this study, a qPCR analysis of a prioritised set of 38 candidate miRNAs in the blood of 830 healthy individuals from the CHARIOT PRO cohort (West London, UK) was undertaken. Here, we identified six differentially expressed miRNAs (hsa-miR-128-3p, hsa-miR-144-5p, hsa-miR-146a-5p, hsa-miR-26a-5p, hsa-miR-29c-3p and hsa-miR-363-3p) in the blood of individuals with low cognitive performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). A pathway enrichment analysis for the six miRNAs indicated involvement of apoptosis and inflammation, relevant in early AD stages. Subsequently, we used whole genome sequencing (WGS) data from 750 individuals from the AD Neuroimaging Initiative (ADNI) to perform a genetic association analysis for polymorphisms within the significant miRNAs’ genes and CSF concentrations of phosphorylated-tau, total-tau, amyloid-β42 and soluble-TREM2 and BACE1 activity. Our analysis revealed 24 SNPs within MIR29C to be associated with CSF levels of amyloid-β42 and soluble-TREM2 and BACE1 activity. Our study shows the potential of a six-miRNA set as diagnostic blood biomarker of subclinical cognitive deficits in AD. Polymorphisms within MIR29C suggest a possible interplay between the amyloid cascade and microglial activation at preclinical stages of 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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".