Abstract 12243: Associations Between Physical Activity and Prognostic Biomarkers in Patients With Stable Coronary Heart Disease in the STABILITY Trial
Bibliographic record
Abstract
Introduction: Physical activity (PA) reduces the risk of events in patients with stable coronary heart disease (CHD). Biomarkers reflecting myocardial dysfunction, renal function and inflammatory activity are associated with outcomes in stable CHD. It is poorly known to what extent the benefits of PA may be linked to biomarker levels. Hypothesis: The association between PA and outcomes may be mediated by processes indicated by changes in the levels of prognostic biomarkers. Methods: At baseline, 15,486 patients with stable CHD participating in the global STABILITY trial, completed a baseline lifestyle questionnaire including self-reporting on hours spent each week on mild, moderate and vigorous exercise, corresponding to approximately 2, 4 and 8 METS, respectively. Plasma levels of high-sensitivity (hs) C-reactive protein (hs-CRP), hs-troponin T (hs-TnT), N-terminal pro-B type natriuretic peptide (NT-proBNP), cystatin-C, growth differentiation factor-15 (GDF-15) and lipoprotein-associated phospholipase A2 (Lp-PLA2) activity were assessed from plasma samples obtained at baseline. Associations between PA and biomarker levels were evaluated after multivariable adjustments (age, gender, traditional clinical cardiovascular risk factors and standard biomarkers including cholesterol levels) with sedentary patients as reference. Results: Associations between levels of PA and hs-CRP, hs-TnT, NT-proBNP, cystatin C, GDF-15 and activity of Lp-PLA2 are shown in the Table, after adjustments for co-variables. PA was independently and inversely associated with all biomarker levels, except for Lp-PLA2. Conclusions: Increasing PA, was independentlyand inversely associated with levels of most clinically important biomarkers, except for Lp-PLA2. The effects of PA on outcomes may partly be explained by disease processes reflected by changes in biomarker levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".