Brain-Derived Neurotrophic Factor Mitigates the Association Between Platelet Dysfunction and Cognitive Impairment
Bibliographic record
Abstract
Background: Platelet hyperactivity is deleterious in coronary artery disease (CAD), requiring lifelong antiplatelet therapy, and is associated with worse cognitive outcomes. Upon activation, platelets release Brain-Derived Neurotrophic Factor (BDNF), a neurotrophin protective against cognitive decline. Given these apparently opposing effects of platelet activation on cognitive health, we investigated whether BDNF levels intercede in the relationship between platelet activation and cognitive function; and whether this relationship is moderated by the presence of CAD. Methods: In this cross-sectional study, 1,280 participants with (n = 673) and without CAD (n = 607) completed the Montreal Cognitive Assessment (MoCA). Plasma BDNF and soluble P-selectin (a marker of platelet activity) levels were assessed using multiplex flow cytometry. Results: In a mediation model, platelet activity was correlated with higher plasma BDNF concentrations (b = 0.53, p < 0.0001). The relationship between sP-selectin and BDNF concentrations was stronger for individuals without CAD (b = 0.71, p < 0.0001) than for CAD participants (b = 0.43, p < 0.0001; pinteraction <0.0001). Higher BDNF concentrations were associated with higher MoCA scores (b = 0.26, p = 0.03). The overall effect of platelet activity on cognitive performance was non-significant (total effect: b = −0.12, p = 0.13), and became significant when accounting for BDNF as a mediating factor (direct effect: b = −0.26, p = 0.01). This resulted in a positive indirect effect of platelet activity (via BDNF) on MoCA scores (b = 0.14, CI 95% 0.02–0.30), that was smaller in CAD participants than in non-CAD participants [Δ −0.07 (95% CI −0.14 to −0.01)]. Conclusions: BDNF released from activated platelets could be a mitigating factor in a negative association between platelet activity and cognitive function.
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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 | 0.001 |
| 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".