Systemic inflammation and brachial artery endothelial function in the Multi-Ethnic Study of Atherosclerosis (MESA)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Inflammation and endothelial dysfunction have been implicated in the pathogenesis of atherosclerotic vascular disease. Brachial artery flow-mediated dilation (FMD) is a reliable, non-invasive method of assessing endothelial function. We hypothesised that increased levels of systemic inflammatory markers are associated with impaired endothelial function as assessed by FMD in a multi-ethnic cohort. METHODS: We assessed brachial artery FMD in 3501 participants (1739 men, 1762 women; median age 61 years) in the Multi-Ethnic Study of Atherosclerosis and measured serum concentrations of interleukin (IL)-6, C reactive protein (CRP) and tumour necrosis factor (TNF)-α receptor 1. Spearman correlation coefficients were used to evaluate the association of each inflammatory marker with FMD, adjusting for the effect of other variables associated with FMD. RESULTS: There was a significant inverse correlation between IL-6 levels and FMD (-0.042; p=0.02) after adjustment for age, gender, race/ethnicity, education, income, low-density lipoprotein, diabetes, glucose, hypertension status and treatment, waist circumference, triglycerides, baseline brachial diameter, recent infection and use of medications that may alter inflammation. There was no significant correlation between CRP and FMD (0.008; p=0.64) or TNF-α receptor 1 and FMD (0.014; p=0.57). There was no evidence of effect modification by race/ethnicity. CONCLUSIONS: In this multi-ethnic cohort, increased levels of the pro-inflammatory cytokine IL-6 were associated with impaired endothelial function assessed by FMD. Elevated IL-6 levels may reflect a state that promotes vascular inflammation and development of subclinical atherosclerosis independent of traditional cardiovascular risk factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".