Impact of lifestyle factors on fracture risk in older patients with cardiovascular disease: a prospective cohort study of 26,335 individuals from 40 countries
Bibliographic record
Abstract
BACKGROUND: fractures are a major health concern among the elderly. People at risk for cardiovascular disease (CVD) are at an increased risk for fractures. The aim of this study was to assess the individual and combined effect of the CVD risk factors of smoking, alcohol consumption and physical activity on fracture risk in a large sample of older individuals with CVD or diabetes with end-organ damage. METHODS: we analysed data for 26,335 adults, aged 55 years or older, who participated in two large antihypertensive drug treatment trials and who had no previous fracture at baseline. Lifestyle factors were assessed by the standardised questionnaire and their individual and combined effects on incident fracture risk were modelled using Cox proportional hazard regression. RESULTS: during the 56-month follow-up, 1,079 incident fractures occurred; 508 (6.51%) among women and 571 (3.08%) among men. Smoking [hazard ratio (HR) 1.52, 95% confidence interval (CI) 1.27-1.82] and low physical activity (HR: 1.19, 95% CI: 1.05-1.36) were associated with an increased risk of any fracture, while high alcohol intake showed a directional, but non-significant, relationship with fracture risk (HR: 1.09, 95% CI: 0.64-1.84). Compared with participants with no lifestyle risk factors, those having one, two, or three risk factors had an increased risk of a future fracture (HR: 1.17, 95% CI: 1.03-1.34 for one risk factor; HR: 1.73, 95% CI: 1.38-2.16 for two risk factors; and HR: 2.37, 95% CI: 0.88-6.36 for three risk factors; P for trend <0.001). CONCLUSIONS: a healthier lifestyle advocated to reduce the risk of CVD is associated with a significant and graded reduction in fracture risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".