Association Between Iron Deficiency (With or Without Anemia) and Risk of Heart Failure in Adults: A Meta-Analysis of Observational and Clinical Studies
Bibliographic record
Abstract
Background: Iron deficiency, even without anemia, worsens heart failure outcomes. Its independent role in heart failure progression remains unclear despite growing evidence. Objective: To assess the link between iron deficiency and adverse outcomes in heart failure via a systematic review and meta-analysis. Methods: This meta-analysis adhered to PRISMA guidelines. A comprehensive search of PubMed, Embase, Scopus, and the Cochrane Library was conducted to identify eligible studies evaluating iron deficiency and HF outcomes. Inclusion criteria comprised adult HF patients, iron deficiency defined by ferritin <100 ng/mL or 100–299 ng/mL with transferrin saturation <20%, and reported outcomes related to mortality, hospitalization, symptom burden, or functional capacity. Pooled hazard ratios (HRs) with 95% confidence intervals (Cis) were calculated using a random-effects model. Subgroup and sensitivity analyses were performed, and risk of bias was assessed using Cochrane and Newcastle-Ottawa tools. Results: Six studies (n = 2,823) were included, comprising four RCTs and two observational cohorts. The pooled analysis showed that iron deficiency was associated with significantly worse HF outcomes (HR 0.85; 95% CI: 0.75–0.97). Subgroup analysis revealed favorable outcomes in RCTs (HR 0.76; 95% CI: 0.64–0.90), particularly among patients with concomitant anemia (HR 0.78; 95% CI: 0.65–0.93). Observational studies, in contrast, showed increased risk in iron-deficient patients (HR 1.68; 95% CI: 1.25–2.26). The association in non-anemic patients was not statistically significant (HR 0.88; 95% CI: 0.72–1.06). Conclusion: Iron deficiency, especially when accompanied by anemia, is a significant predictor of adverse clinical outcomes in heart failure. These findings support routine iron status screening and the consideration of iron repletion therapy as part of comprehensive HF management strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".