The Clinical Significance of Interleukin-6 in Heart Failure: Results from the BIOSTAT-CHF Study
Bibliographic record
Abstract
AIMS: Inflammation is a central process in the pathophysiology of heart failure (HF), but trials targeting tumour necrosis factor (TNF)-α were largely unsuccessful. Interleukin (IL)-6 is an important inflammatory mediator and might constitute a potential pharmacologic target in HF. However, little is known regarding the association between IL-6 and clinical characteristics, outcomes and other inflammatory biomarkers in HF. We thus aimed to identify and characterize these associations. METHODS AND RESULTS: Interleukin-6 was measured in 2329 patients [89.4% with a left ventricular ejection fraction (LVEF) ≤ 40%] of the BIOSTAT-CHF cohort. The primary outcome was all-cause mortality and HF hospitalization during 2 years, with all-cause, cardiovascular (CV), and non-CV death as secondary outcomes. Approximately half (56%) of all included patients had plasma IL-6 values greater than the previously determined 95th percentile of normal values at baseline. Elevated N-terminal pro-brain natriuretic peptide, procalcitonin and hepcidin, younger age, TNF-α/IL-1-related biomarkers, or having iron deficiency, atrial fibrillation and LVEF > 40% independently predicted elevated IL-6 levels. IL-6 independently predicted the primary outcome [HR (95% confidence interval) per doubling: 1.16 (1.11-1.21), P < 0.001], all-cause mortality [1.22 (1.16-1.29), P < 0.001] and CV as well as non-CV mortality [1.16 (1.09-1.24), P < 0.001; 1.31 (1.18-1.45), P < 0.001], but did not improve discrimination in previously published risk models. CONCLUSIONS: In a large, heterogeneous cohort of HF patients, elevated IL-6 levels were found in more than 50% of patients and were associated with iron deficiency, reduced LVEF, atrial fibrillation and poorer clinical outcomes. These findings warrant further investigation of IL-6 as a potential therapeutic target in specific HF subpopulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".