Abstract TMP55: Abdominal Obesity Predicts Stroke Risk in the Framingham Study
Bibliographic record
Abstract
Background: Previous studies have suggested that measurements of abdominal fat, such as waist circumference or waist-to-hip ratio, more accurately predict risk of stroke than body mass index (BMI). We assessed the risk of ischemic stroke associated with BMI, waist circumference, and waist-to-hip ratio in a community sample. Methods: We pooled Framingham Heart Study (FHS) participant data from FHS clinic examinations for the Original cohort (Exam 21, date range 1988-1992) and Offspring cohort (Exam 4, 1987-1991, and Exam 7, 1998-2001). Offspring participants who did not have stroke at the end of the first 10-year observation period could contribute data to the subsequent observation period. We included stroke-free participants 45 years of age or older who obtained measurements of BMI (kg/m 2 ) and both waist and hip circumference. Multivariable Cox proportional hazards regression models were used to separately relate BMI, waist circumference, and waist-to-hip ratio to risk of incident ischemic stroke over 10 years. Results: We analyzed data from 6,533 observation periods (mean age of participants 62±10 years, 54% female) with mean follow up time of 9.2 ±2 years. There were 240 (3.7%) incident ischemic stroke events. Each of the three body weight measurements was associated with increased stroke risk in models that adjusted for age and sex (Table). In full models, additionally adjusting for vascular risk factors that are more frequent in obese persons, only waist-to-hip ratio predicted risk of ischemic stroke. The hazard ratio (HR) per standard deviation increase in waist-to-hip ratio was 1.18, 95% CI 1.02-1.37, p=0.030. Conclusions: Higher waist-to-hip ratio predicted 10-year risk of ischemic stroke. Waist-to-hip ratio is a simple measure of abdominal obesity that should be used in future studies investigating weight loss interventions for stroke prevention.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".