Plasma BDNF/Irisin Ratio Associates with Cognitive Function in Older People
Bibliographic record
Abstract
Background: Reliable blood biomarkers are crucial for early detection and treatment evaluation of cognitive impairment, including Alzheimer's disease and other dementias. Objective: To examine whether plasma biomarkers and their combination are different between older people with mild cognitive impairment (MCI) and cognitively normal individuals, and to explore their relations with cognitive performance. Methods: This cross-sectional study included 250 older adults, including 124 participants with MCI, and 126 cognitively normal participants. Plasma brain-derived neurotrophic factor (BDNF), irisin and clusterin were measured, and BDNF/irisin ratio was calculated. Global cognition was evaluated by the Montreal Cognitive Assessment. Results: Plasma irisin levels, but not BDNF, were significantly different between MCI group and cognitively normal group. Higher irisin concentration was associated with an increased probability for MCI both before and after controlling covariates. By contrast, plasma BDNF concentration, but not irisin, was linearly correlated with cognitive performance after adjusting for covariates. Higher BDNF/irisin ratios were not only correlated with better cognitive performance, but also associated with lower risks of MCI, no matter whether we adjusted for covariates. Plasma BDNF and irisin concentrations increased with aging, whereas BDNF/irisin ratios remained stable. No significant results of clusterin were observed. Conclusions: Plasma BDNF/irisin ratio may be a reliable indicator which not only reflects the odds of the presence of MCI but also directly associates with cognitive performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".