Inflammatory Biomarkers in Heart Failure: Clinical Perspectives on hsCRP, IL-6 and Emerging Candidates
Bibliographic record
Abstract
PURPOSE OF REVIEW: Heart failure (HF) remains a leading cause of morbidity and mortality worldwide. Increasing evidence highlights that systemic low-grade inflammation is a key pathophysiological driver of HF. This review seeks to examine the diagnostic and therapeutic relevance of inflammatory biomarkers - specifically interleukin-6 (IL-6) and high-sensitivity C-reactive protein (hsCRP) - and evaluate their potential for improving risk stratification and enabling personalized treatment approaches in HF. RECENT FINDINGS: IL-6 and hsCRP have emerged as important markers of residual inflammatory risk in HF. Elevated levels of these biomarkers are associated with increased risk of incident HF and adverse outcomes in established disease. While hsCRP is as a downstream marker of inflammation with no causal involvement, Mendelian randomization studies support a causal role of IL-6 signaling in the development of HF and coronary artery disease. Recent and ongoing clinical trials support the concept of targeting inflammatory pathways as a therapeutic strategy in selected HF populations. Inflammatory biomarkers, particularly IL-6 and hsCRP, are promising tools for advancing precision medicine in HF by improving individual risk assessment and guiding anti-inflammatory interventions. Further large-scale studies are needed to validate the integration of inflammatory biomarkers into clinical algorithms for HF and explore their potential role in future guideline recommendations and personalized prevention strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".