Association of Blood MicroRNA Expression and Polymorphisms with Cognitive and Biomarker Changes in Older Adults
Bibliographic record
Abstract
BACKGROUND: Identifying individuals before the onset of overt symptoms is key in the prevention of Alzheimer's disease (AD). OBJECTIVES: Investigate the use of miRNA as early blood-biomarker of cognitive decline in older adults. DESIGN: Cross-sectional. SETTING: Two observational cohorts (CHARIOT-PRO, Alzheimer's Disease Neuroimaging Initiative (ADNI)). PARTICIPANTS: 830 individuals without overt clinical symptoms from CHARIOT-PRO and 812 individuals from ADNI. MEASUREMENTS: qPCR analysis of a prioritised set of 38 miRNAs in the blood of individuals from CHARIOT-PRO, followed by a brain-specific functional enrichment analysis for the significant miRNAs. In ADNI, genetic association analysis for polymorphisms within the significant miRNAs' genes and CSF levels of phosphorylated-tau, total-tau, amyloid-β42, soluble-TREM2 and BACE1 activity using whole genome sequencing data. Post-hoc analysis using multi-omics datasets. RESULTS: Six 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) were downregulated in the blood of individuals with low cognitive performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The pathway enrichment analysis indicated involvement of apoptosis and inflammation, relevant in early AD stages. Polymorphisms within genes encoding for hsa-miR-29c-3p and hsa-miR-146a-5p were associated with CSF levels of amyloid-β42, soluble-TREM2 and BACE1 activity, and 21 variants were eQTL for hippocampal MIR29C expression. CONCLUSIONS: six miRNAs may serve as potential blood biomarker of subclinical cognitive deficits in AD. Polymorphisms within these miRNAs 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".