Neutrophil Gelatinase-Associated Lipocalin (NGAL) in Chronic Cardiorenal Failure is Correlated with Endogenous Erythropoietin Levels and Decreases in Response to Low-Dose Erythropoietin Treatment
Bibliographic record
Abstract
BACKGROUND: Neutrophil-gelatinase associated lipocalin (NGAL), a tubular injury marker, is associated with iron metabolism in hemodialysis patients. We investigated whether serum NGAL levels reflect iron metabolism in combined chronic heart failure and chronic kidney disease (CHF/CKD) and whether treatment with low-dose erythropoietin stimulating agent (ESA) modulates NGAL levels. METHODS: In the EPOCARES trial (ClinTrialsNCT00356733) serum NGAL, hepcidin-25, transferrin saturation (TSAT), reticulocyte hemoglobin content (Ret-He) and endogenous erythropoietin (EPO) levels were measured. RESULTS: Baseline serum NGAL levels correlated with cystatin C (r=0.767, p<0.001) and baseline EPO levels (r=-0.395, p=0.003). There was no correlation with baseline TSAT, Ret-He, and hepcidin-25 levels. After two weeks, NGAL levels decreased in the ESA-group (p=0.02), while there was no change in the no-ESA group (p=0.62). The magnitude in NGAL decrease in the ESA-group correlated with baseline EPO levels (r=0.431, p=0.01). CONCLUSIONS: In contrast to in HD patients, in combined CKD/ CHF, serum NGAL levels did not correlate with iron metabolism, hence NGAL might reflect tubular damage in these patients. NGAL levels inversely correlated with baseline EPO levels and decreased in response to short-term ESA treatment, which might reflect an effect of ESA on tubular damage. These findings need to be confirmed and alternative explanations should be evaluated.
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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) | 0.000 | 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 | 0.000 |
| 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".