Stroke Incidence According to Cardiorespiratory Fitness: A Cohort Study of 483,379 Hypertensive Patients
Bibliographic record
Abstract
Abstract Objectives We assessed stroke incidence in hypertensive patients according to cardiorespiratory fitness (CRF) and changes in CRF. Methods A prospective cohort study of 483,379 US Veterans. Participants completed a maximal standardized Exercise Treadmill test (ETT) performed within the Veterans Affairs medical centers across the United States between 1999 and 2020. None exhibited evidence of unstable cardiovascular disease during the ETT. Participants were stratified into 5 age-and-gender specific CRF categories based on the peak metabolic equivalents (METs) achieved. A subgroup of participants with two ETT evaluations (n=110, 576) were also assigned to 4 categories based on MET changes from the initial ETT to the final ETT. Multivariable Cox models, adjusted for age, and co-morbidities were used to estimate HRs and 95% CIs for stroke risk. Results The mean age ± standard deviation (SD) was 59.4±9.0 years. During the median follow-up time of 10.6 years (5,182,179 person-years), there were 15,925 stroke events with an average annual rate of 3.1 events per 1,000 person-years. In a final adjusted model, relatively poor CRF was the strongest predictor of stroke risk than any other comorbidity (HR: 2.24; 95% CI: 2.10-2.40; P< 0.001). For each 1-MET higher exercise capacity, the risk was 10% lower (0.90, 95% CI 0.90-0.91, p<0.001). Compared to the Least-fit, stroke risk was 23% lower for Low-fit individuals (HR 0.77; 95% CI, 0.73-0.80; p<0.001); and declined progressively to 55% for those in the highest CRF category (HR 0.45; 95% CI 0.42-0.48; p<0.001). We also assessed stroke incidence according to change in CRF. Compared to fit individuals during both evaluations, the risk was 27% higher for those who became unfit (HR 1.27, 95% CI 1.15-1.41, p<0.001), and not significantly different for unfit who became fit (HR 1.10, 95% CI 0.97-1.25, p=0.13). Conclusions Poor CRF was the strongest predictor of stroke incidence in hypertensive patients, regardless of age race, or gender. The association was independent, inverse, and graded for all stroke types. Changes in CRF over time reflected inverse changes in stroke risk, suggesting that risk of stroke can be modulated by improved CRF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".