Markers of Iron Metabolism and Outcomes in Patients with Heart Failure: A Systematic Review
Bibliographic record
Abstract
Iron deficiency (ID) in conjunction with heart failure (HF) poses a challenge for clinicians and is associated with worse HF outcomes. Treatment of ID with IV iron supplementation for patients with HF has demonstrated benefits in quality of life (QoL) and HF-related hospitalizations. The aim of this systematic review was to summarize the evidence linking iron metabolism biomarkers with outcomes in patients with HF to assist in the optimal use of these biomarkers for patient selection. A systematic review of observational studies in English from 2010 to 2022 was conducted using PubMed, with keywords of "Heart Failure" and respective iron metabolism biomarkers ("Ferritin", "Hepcidin", "TSAT", "Serum Iron", and "Soluble Transferrin Receptor"). Studies pertaining to HF patients, with available quantitative data on serum iron metabolism biomarkers, and report of specific outcomes (mortality, hospitalization rates, functional capacity, QoL, and cardiovascular events) were included, irrespective of left ventricular ejection fraction (LVEF) or other HF characteristics. Clinical trials of iron supplementation and anemia treatment were removed. This systematic review was conducive to formal assessment of risk of bias via Newcastle-Ottawa Scale. Results were synthesized based on their respective adverse outcomes and iron metabolism biomarker(s). Initial and updated searches identified 508 unique titles once duplicates were removed. The final analysis included 26 studies: 58% focused on reduced LVEF; age range was 53-79 years; males composed 41-100% of the reported population. Statistically significant associations of ID were observed with all-cause mortality, HF hospitalization rates, functional capacity, and QoL. Increased risk for cerebrovascular events and acute renal injury have also been reported, but these findings were not consistent. Varying definitions of ID were utilized among the studies; however, most studies employed the current European Society of Cardiology criteria: serum ferritin < 100 ng/mL or the combination of ferritin between 100-299 ng/mL and transferrin saturation (TSAT) < 20%. Despite several iron metabolism biomarkers demonstrating strong association with several outcomes, TSAT better predicted all-cause mortality, as well as long-term risk for HF hospitalizations. Low ferritin was associated with short-term risk for HF hospitalizations, worsening functional capacity, poor QoL, and development of acute renal injury in acute HF. Elevated soluble transferrin receptor (sTfR) levels were associated with worse functional capacity and QoL. Finally, low serum iron was significantly associated with increased risk for cardiovascular events. Considering the lack of consistency among the iron metabolism biomarkers for association with adverse outcomes, it is important to incorporate additional biomarker data, beyond ferritin and TSAT, when assessing for ID in HF patients. These inconsistent associations question how best to define ID to ensure proper treatment. Further research, potentially tailored to specific HF phenotypes, is required to optimize patient selection for iron supplementation therapy and appropriate targets for iron stores replenishment.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".