Influence of age on the association of vascular risk factors with acute stroke (INTERSTROKE): a case–control study
Bibliographic record
Abstract
Background The absolute burden of stroke is increasing due to an ageing population, as well as an increased incidence of stroke in young adults. We aimed to determine whether age modifies the magnitude of association between vascular risk factors and stroke in the INTERSTROKE study. Methods INTERSTROKE is an international case–control study of risk factors for first acute stroke. Cases and controls (matched by age and sex) were recruited in 32 countries (between Jan 11, 2007, and Aug 8, 2015). Participants completed a clinical assessment and provided blood and urine samples within 72 h of recruitment. Odds ratios (ORs) for vascular risk factors and their population attributable fractions (PAFs) were calculated among age groups. We tested for an interaction of age by each risk factor. Findings Among 26 950 participants, the mean age of cases was 62·2 years (SD 13·6) and of controls 61·3 years (13·3). Increasing age was associated with a significant increased prevalence for seven vascular risk factors (hypertension, physical inactivity, diabetes, atrial fibrillation, high waist-to-hip ratio, high apolipoprotein B concentration [p<0·0001 for all], and obesity [p=0·016]), reduced prevalence for four vascular risk factors (smoking, alcohol use, psychosocial stress [p<0·0001 for all], and unhealthy diet [p=0·0081]) and unchanged prevalence for one vascular risk factor (depression). Increasing age was associated with a reduced magnitude of OR of stroke for hypertension (p interaction <0·0001), high apolipoprotein B concentration (p interaction <0·001), high waist-to-hip ratio (p interaction 0·011), alcohol use (p interaction <0·0001), and psychosocial stress (p interaction =0·033). No vascular risk factor was associated with a higher odds of stroke with increased age. Hypertension, high waist-to-hip ratio, and physical inactivity accounted for the largest PAF among all age groups. Interpretation Vascular risk factors exhibit different patterns of association with stroke by age, with consequent variations in their relative PAF. This information could be used to prioritise risk factor screening and modification by age group. Funding Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada; Canadian Stroke Network; AFA Insurance, Health Research Board Ireland; Swedish Research Council; Swedish Heart and Lung Foundation; The Health & Medical Care Committee of the Regional Executive Board, Region Västra Götaland (Sweden); AstraZeneca; Boehringer Ingelheim (Canada); Pfizer (Canada); MSD; Chest, Heart, and Stroke Scotland; and The UK Stroke Association.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".