Targeting Inflammation and Iron Deficiency in Heart Failure: A Focus on Older Adults
Bibliographic record
Abstract
Background/Objectives: Heart failure (HF) is a leading cause of morbidity and mortality worldwide, with a higher prevalence among older adults. Iron deficiency (ID), affecting up to 50% of HF patients, is closely linked to chronic inflammation, exacerbating HF outcomes. This review aims to explore the interplay between inflammation, ID, and HF, focusing on older patients, and to identify therapeutic gaps and emerging treatment strategies. Methods: A comprehensive review of the literature was conducted, emphasizing the pathophysiological mechanisms of inflammation and ID in HF, the challenges of current diagnostic criteria, and the limitations of available treatments. Emerging pharmacological and diagnostic approaches were analyzed. Results: Chronic inflammation in HF, particularly in older adults, promotes functional ID through elevated hepcidin levels, impairing iron availability and worsening anemia. Current diagnostic criteria, relying heavily on ferritin, often misclassify ID due to inflammation. Intravenous (IV) iron therapy shows clinical benefits in patients with <50% left ventricular ejection fraction (LVEF), but the evidence is limited in heart failure with preserved ejection fraction (HFpEF). Emerging therapies, such as Sodium-Glucose Cotransporter-2 inhibitors (SGLT2is) and prolyl hydroxylase inhibitors like Roxadustat, offer promising avenues to improve iron metabolism and outcomes. Conclusions: ID and inflammation significantly impact HF progression, particularly inolder adults. Refining diagnostic criteria and exploring innovative therapies are critical to addressing these challenges. Future research should prioritize personalized approaches targeting inflammation and ID, especially in underrepresented populations, such as HFpEF and elderly patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".