Interleukin-6 and Cardiovascular Events in Healthy Adults
Bibliographic record
Abstract
Background: Elevated interleukin (IL)-6 levels have been linked to adverse outcomes in patients with and without baseline cardiovascular disease (CVD). Objectives: The purpose of this study was to examine the association between circulating IL-6 levels and CVD events without baseline CVD across racial and ethnic groups. Methods: We conducted an observational analysis utilizing the MESA (Multi-Ethnic Study of Atherosclerosis), a multicenter, prospective community-based study of CVD at baseline from four racial and ethnic groups. IL-6 levels were measured at the time of enrollment (visit 1) and were divided into 3 terciles. Patient baseline characteristics and outcomes, including all-cause mortality, CV mortality, heart failure, and non-CV mortality, were included. Cox proportional hazard regression models were used to assess associations between IL-6 levels and study outcomes with IL-6 tercile 1 as reference. Results: Of 6,622 individuals, over half were women (53%) with a median age of 62 (IQR: 53-70) years. Racial and ethnic composition was non-Hispanic White (39%) followed by African American (27%), Hispanic (22%), and Chinese American (12%). Compared to tercile 1, participants with IL-6 tercile 3 had a higher adjusted risk of and all-cause mortality (HR: 1.98 [95% CI: 1.67-2.36]), CV mortality (HR: 1.55 [95% CI: 1.05-2.30]), non-CV mortality (HR: 2.05 [95% CI: 1.65-2.56]), and heart failure (HR: 1.48 [95% CI: 0.99-2.19]). When tested as a continuous variable, higher levels of IL-6 were associated with an increased risk of all individual outcomes. Compared to non-Hispanic White participants, the unadjusted and adjusted risk of all outcomes across all races and ethnicities was similar across all IL-6 terciles. Conclusions: High levels of circulating IL-6 are associated with worse CV outcomes and increased all-cause mortality consistently across all racial and ethnic groups.
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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 | 0.000 | 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 | 0.001 |
| 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".