Hepcidin-25 is a Marker of the Response Rather than Resistance to Exogenous Erythropoietin in Chronic Kidney Disease/Chronic Heart Failure Patients
Bibliographic record
Abstract
AIMS: Erythropoietin (EPO) resistance, an important cause of anaemia in patients with heart and renal failure, is associated with increased mortality. The hypothesis of the present study was that exogenous EPO decreases hepcidin levels and that the decrease in hepcidin levels upon EPO treatment is related to the bone marrow response. METHODS AND RESULTS: In the EPOCARES trial, patients with renal failure (glomerular filtration rate 20-70 mL/min), heart failure, and anaemia were randomized to receive 50 IU/kg/week EPO (n = 20) or not (n = 13). Haemoglobin (Hb), hepcidin-25, ferritin, reticulocytes, serum transferrin receptor (sTfR), IL-6, and high-sensitivity C-reactive protein were measured at baseline and during treatment. Hepcidin-25 was measured by weak cation exchange chromatography/matrix assisted laser desorption ionization time-of-flight mass spectrometry. Baseline hepcidin levels were increased compared with a healthy reference population and were inversely correlated with Hb (r(2) = 0.18, P = 0.02), and positively with ferritin (r(2) = 0.51, P < 0.001), but not with renal function, high-sensitivity C-reactive protein or IL-6. Erythropoietin treatment increased reticulocytes (P < 0.001) and sTfR (P < 0.001), and decreased hepcidin (P < 0.001). Baseline hepcidin levels and the magnitude of the decrease in hepcidin correlated with the increase in reticulocytes (r(2) = 0.23, P = 0.03) and sTfR (r(2) = 0.23, P = 0.03) and also with the Hb response after 6 months (r(2) = 0.49, P = 0.001). CONCLUSION: In this group of patients with combined heart and renal failure and anaemia, increased hepcidin levels were associated with markers of iron load and not with markers of inflammation. The (change in) hepcidin levels predicted early and long-term bone marrow response to exogenous EPO. In our group hepcidin seems to reflect iron load and response to EPO rather than inflammation and EPO resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".