Understanding cardiovascular disease in older adults with epilepsy
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to investigate the distribution of social, lifestyle/behavior, and chronic disease risk factors for cardiovascular disease (CVD) in people with epilepsy as compared to the general population. We also measured the cross-sectional association between epilepsy and CVD in older adults, with and without adjustments for a history of stroke. METHODS: We analyzed data for 44 817 participants in the Canadian Longitudinal Study on Aging, including 751 individuals with a lifetime history of epilepsy. We modeled associations using ordinal and binomial logistic regression, as well as log-binomial regression, with multiple imputation for missing data. We measured the attributable fraction of CVD burden due to stroke. RESULTS: The majority of the CVD risk factors were significantly more prevalent in people with epilepsy as compared to the general population without epilepsy, independent of age and sex. After adjusting for a history of stroke, people with epilepsy had a significantly higher prevalence of heart disease (prevalence ratio [PR] = 1.27, 95% confidence interval [CI] = 1.02-1.57) and peripheral vascular disease (PR = 1.88, 95% CI = 1.50-2.36). Stroke accounted for 36% (95% CI = 19.85-48.76) of the increased prevalence of any CVD among people with epilepsy, similar to the 32% (95% CI = 27.82-36.25) among people without epilepsy. After adjustment for all other CVD risk factors, peripheral vascular disease remained significantly more prevalent (PR = 1.65, 95% CI = 1.28-2.12) in people with epilepsy as compared to those without. SIGNIFICANCE: CVD risk factors are more prevalent in people with epilepsy, independent of age and sex, and the association between epilepsy and CVD is independent of the association between epilepsy and stroke. The association between peripheral vascular disease and epilepsy may differ from the associations with other types of CVD. These findings are important steps in more comprehensively understanding the origins of CVD in people with epilepsy.
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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 | 0.001 |
| 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) | 0.001 | 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 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".