Abstract P389: Smoking, Biomarkers and Ischemic Stroke Risk: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) Cohort
Bibliographic record
Abstract
Introduction: Identifying biological pathways that mediate the increased stroke risk in smokers may allow study of novel strategies to reduce risk. In the REGARDS study, we evaluated the association of smoking status with incident stroke and assessed biomarkers as mediators of smoking related stroke. Methods: 30,239 black and white adults aged 45 and older were enrolled in 2003-7, and followed for incident stroke. Biomarkers were measured in a case cohort substudy and compared across groups of smoking status (never, former or current). Hazard ratios (HRs) of stroke by smoking status and by pack years were calculated using Cox proportional hazards models. Results: Among 1,224 participants with biomarker data (mean age 62, 50% male, 50% black) there were 531 incident stroke cases over 12.9 yrs maximal follow up. Compared to never smokers, the multivariable (age, race, sex and Framingham risk factors) adjusted HR of stroke in current smokers was 1.30 (95% CI 1.11, 1.53) and in former smokers was 1.18 (95% CI 1.05, 1.32). The association of pack years with stroke was substantial in current smokers and weak in former smokers in the adjusted model (Table). Among 7 biomarkers, white blood count, interleukin-6 (IL-6) and fibrinogen were higher in former and current smokers, and with increasing pack years in both groups (data not shown). The association of pack years with stroke in current smokers was partly attenuated with adjustment for IL-6, but not white count or fibrinogen. Among former smokers, white count fully attenuated the weak association. Conclusions: Former and current smoking, and pack years (primarily among current smokers) were stroke risk factors in a contemporary biracial cohort. Among both current and former smokers, smoking-related stroke risk was attenuated after adjustment for IL-6 and white count respectively, suggesting biological pathways for intervention.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".