Inflammatory markers and the risk of vascular complications and mortality in type 2 diabetes mellitus
Bibliographic record
Abstract
Purpose: There are few data assessing the relationship between circulating levels of C-reactive protein (CRP), fibrinogen, and interleukin-6 (IL-6) and the risk of vascular complications in individuals with type 2 diabetes mellitus (T2DM). We studied the associations between these inflammatory markers and the risk of major CV events (CV death, myocardial infarction or stroke), microvascular complications and death in patients T2DM who participated in the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial. Methods: Baseline high sensitivity CRP, fibrinogen and IL-6 levels were determined in a case-cohort study (n=3,865), nested within the ADVANCE trial. Results: During 5 years of follow-up, 709 patients suffered a major CV event, 439 a microvascular complication and 706 died. All 3 markers were associated with an increased risk of CV events and death in analyses adjusting for age, sex and treatment groups. After further adjustment, for other potential confounders and for each other, only IL-6 was an independent predictor of these outcomes (hazard ratio [HR] for CV events 1.37 per 1 standard deviation [SD] increase in log IL-6, 95% confidence interval [CI] 1.24-1.51; HR for death 1.35, 95% CI 1.23-1.49). This increased hazard was seen in patients with and without prior CV disease (figure, HR per 1 SD increase in log IL-6). IL-6 significantly improved the prediction of CV events and death using reclassification statistics (net reclassification improvement in continuous models 23% for CV events and 30% for death). After adjustment, none of the markers predicted microvascular complications. Death stratified by prior CV disease Conclusions: IL-6, but not CRP or fibrinogen, levels add significantly to the prediction of CV events and mortality in individuals with T2DM.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".