PLASMA BDNF/IRISIN RATIO ASSOCIATES WITH COGNITIVE FUNCTION IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Objectives To examine whether plasma biomarkers brain-derived neurotrophic factor (BDNF), irisin, clusterin and BDNF/irisin ratio (BIR) could differentiate people with mild cognitive impairment (MCI) from cognitively normal (CN) individuals, and to explore their relations with cognitive performance. Methods We included 124 participants with MCI and 126 CN participants from a community-based aged and cognitive health cohort. Plasma BDNF, irisin and clusterin were measured, and BIR was calculated. Global cognition was evaluated with Montreal Cognitive Assessment. T-tests, logistic regressions, and linear regressions were used to explore the relations between plasma biomarkers and cognitive function. Results The plasma levels of irisin, but not BDNF, was significantly different between MCI and CN groups. Higher irisin concentration was associated with increased probability for MCI (OR: 1.06, p = 0.004) after adjusting for covariates. By contrast, BDNF, but not irisin, was linearly correlated with cognitive performance (β = 0.14, p = 0.033). BIR values were positively correlated with cognitive performance (β= 0.14, p = 0.036), and significant differences on BIR values existed between MCI and CN groups. The MCI risk decreased by 53% (OR=0.47, p = 0.043) with each unit increase in BIR values after adjusting for covariates. Plasma BDNF and irisin concentrations increased with aging, whereas BIR values remained stable across the ages. No significant results of clusterin were observed in the above analyses. Conclusion Plasma BIR is a potentially reliable indicator which not only reflects the odds of the presence of MCI but also directly associates with cognitive performance in the aged population.
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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 | 0.001 |
| 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".