Elevated Neutrophil Gelatinase Lipocalin Levels Are Associated With Increased Oxidative Stress in Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Administration of intravenous iron is an essential treatment of anemia in hemodialysis patients, but it may lead to oxidative stress and increased morbidity and mortality. There is evidence that neutrophil gelatinase-associated lipocalin (NGAL) is protective against oxidative stress and thus the aim of the present study was to investigate the relationship between plasma NGAL and advanced oxidative protein products (AOPP) in hemodialysis patients treated with intravenous iron. METHODS: In a prospective study, 47 hemodialysis patients (mean age 63 years, SD = 13.6; 40% women) were enrolled from two separate hospitals. Oxidative stress was induced by an intravenous administration of 100 mg iron saccharate 0.5 h after the start of dialysis. Blood samples were drawn at the beginning of the dialysis, 0.5 h after iron administration and at the end of dialysis. NGAL levels were measured from the first blood sample, AOPP levels were measured from all blood samples. RESULTS: Our results showed that higher NGAL and AOPP levels at the beginning of the dialysis, prior to iron administration, significantly predicted higher levels of AOPP toward the end of dialysis, (β = 0.355, SE = 0.054, P = 0.035; β = 0.297, SE = 0.159, P = 0.043, respectively). CONCLUSIONS: Our results suggest that higher level of NGAL is a risk factor for oxidative stress, as measured by AOPP levels, in dialysis patients receiving intravenous iron. Our findings could identify dialysis patients who are at higher risk from iron supplementation via measurement of NGAL levels.
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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 | 0.000 | 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".