High-Intensity Exercise Training Improves Basal Platelet Prostacyclin Sensitivity and Potentiates the Response to Dual Anti-Platelet Therapy in Postmenopausal Women
Bibliographic record
Abstract
The risk of thrombotic events dramatically increases with age and may be accelerated in women by the cessation of endogenous estrogen production at menopause. Patients at risk of thrombosis are prescribed dual anti-platelet therapy (DAPT; aspirin and a P2Y12 antagonist) and are encouraged to participate in regular physical activity, as these modalities improve nitric oxide and prostacyclin-mediated inhibition of platelet aggregation. Methods: We assessed prostacyclin sensitivity as well as basal platelet reactivity with and without in vitro DAPT before and after an 8-week high-intensity exercise training program in 13 healthy, sedentary postmenopausal women. The training intervention consisted of three 1 h sessions per week. Isolated platelets were analyzed for thromboxane A2 receptor, thromboxane A2 synthase, cyclooxygenase-1, and prostacyclin receptor protein expression. Additionally, plasma 6-keto prostaglandin F1α and thromboxane B2 levels were determined. Results: Exercise training made platelets more sensitive to the inhibitory effects of prostacyclin on thromboxane-, collagen-, and adenosine 5′-diphosphate (ADP)-induced aggregation, as well as thrombin-receptor activator peptide 6- and ADP-induced aggregation with DAPT. However, there was no change in protein expression from isolated platelets or plasma thromboxane B2 and prostacyclin levels following training. Conclusion: Together, these findings emphasize the importance of promoting physical activity as a tool for reducing thrombotic risk in postmenopausal women and suggest that training status should be considered when prescribing DAPT in this cohort.
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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 | 0.000 |
| 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".