Association of Educational Level with Inflammatory Markers in the Framingham Offspring Study
Bibliographic record
Abstract
Socioeconomic position consistently predicts coronary heart disease; however, the biologic mechanisms that may mediate this association are not well understood. The objective of this study was to determine whether socioeconomic position (measured as educational level) is associated with inflammatory risk factors for coronary heart disease, including C-reactive protein, interleukin-6, soluble intercellular adhesion molecule-1, monocyte chemoattractant protein-1, and P-selectin. The study sample included 2,729 participants (53.4% women; mean age, 62 +/- 10 years) from the US Framingham Offspring Study cohort who attended examination cycles 3 (1984-1987) and 7 (1998-2001) and provided educational attainment data. Inflammatory markers were measured in fasting serum samples. Multivariable linear regression analyses were performed, adjusting for potential confounders including age, sex, and clinical risk factors. In age- and sex-adjusted analyses, educational attainment was significantly inversely associated with C-reactive protein (p < 0.0001), interleukin-6 (p < 0.0001), soluble intercellular adhesion molecule-1 (p < 0.0001), and monocyte chemoattractant protein-1 (p = 0.0004). After further adjustment for clinical risk factors, educational level remained significantly associated with C-reactive protein (p = 0.0002), soluble intercellular adhesion molecule-1 (p = 0.01), and monocyte chemoattractant protein-1 (p = 0.01). In conclusion, educational attainment is associated with inflammatory risk factors for coronary heart disease. The association provides evidence suggestive of a biologic pathway by which socioeconomic position may predispose to coronary heart disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".