Vascular risk factors and stroke risk across the life span: A population-representative study of half a million people
Bibliographic record
Abstract
Background:The incidence of stroke in developed countries is increasing selectively in young individuals, but whether this is secondary to traditional vascular risk factors is unknown.Methods:We used the Canadian Community Health Survey from 2000 to 2016 to create a large population-representative cohort of individuals over the age of 30 and free from prior stroke. All analyses were stratified by age decile. We linked with administrative databases to determine emergency department visits or hospitalizations for acute stroke until December 2017. We calculated time trends in risk factor prevalence (hypertension, diabetes, obesity, and smoking) using meta-regression. We used Cox proportional hazard models to evaluate the association between vascular risk factors and stroke risk, adjusted for demographic, co-morbid, and social variables. We used competing risk regression to account for deaths and calculated population-attributable fractions. In a sensitivity analysis, we excluded those with prior heart disease or cancer.Results:We included 492,400 people in the analysis with 8865 stroke events over a median follow-up time of 8.3 years. Prevalence of hypertension, diabetes, and obesity increased over time while smoking decreased. Associations of diabetes, hypertension, and obesity with stroke risk were progressively stronger at younger age (adjusted hazard ratio for diabetes was 4.47, 95% confidence interval (CI) = 1.95–10.28 at age 30–39, vs 1.21, 95% CI = 0.93–1.57 at age 80+), although the obesity association was attenuated with adjustment. Smoking was associated with higher risk of stroke without a gradient across age deciles, although had the greatest population-attributable fraction at younger age. The hazard ratio for stroke with multiple concurrent risk factors was much higher at younger age (adjusted hazard ratio for 3–4 risk factors was 8.60, 95% CI = 2.97–24.9 at age 30–39 vs 1.61, 95% CI = 0.88–2.97 at age 80+) and results were consistent when accounting for the competing risk of death and excluding those with prior heart disease or cancer.Conclusions:Diabetes and hypertension were associated with progressively elevated relative risk of stroke in younger individuals and prevalence was increasing over time. The association of obesity with stroke was not significant after adjustment for other factors. Smoking had the greatest prevalence and population-attributable fraction for stroke at younger age. Our findings assist in understanding the relationship between vascular risk factors and stroke across the life span and planning public health measures to lower stroke incidence in the young.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".