Is hepcidin‐25 a predictor of atherosclerosis in hemodialysis patients?
Bibliographic record
Abstract
Atherosclerotic cardiovascular disease is an important cause of mortality and morbidity in hemodialysis patients. Iron accumulation in arterial wall macrophages is increased in atherosclerotic lesions. Hepcidin is a key hepatic hormone regulating iron balance. It inhibits iron release from macrophages and iron absorption from enterocytes by binding and inactivating the cellular iron exporter ferroportin. The aim of this study is to investigate the relation of hepcidin-25, iron parameters, and atherosclerosis measured by carotid intima media thickness (CIMT) in hemodialysis patients. Eighty-two hemodialysis patients were enrolled in this cross-sectional study. Predialysis blood samples were centrifuged at 1500 g and 4°C for 10 minutes and stored at -80°C for the measurement of hepcidin-25. DRG hepcidin enzyme-linked immunosorbent assay kit was used for the measurement of hepcidin-25. Ultrasonographical B-mode imaging of bilateral carotid arteries was performed with a high-resolution real-time ultrasonography (Mindray DC7). Mean age of the study population was 57.90 ± 16.08 years and 43.9% were men. Total study population was grouped into two according to median value of hepcidin-25. There was no difference between groups with respect to age, dialysis vintage, and C-reactive protein. CIMT was found to be statistically significantly higher in low hepcidin-25 group. In correlation analysis, CIMT was found to be correlated with age (P < 0.01, R = 0.33) and hepcidin-25 (P < 0.01, R = 0.46). In linear regression analysis, age (β = 0.31) and hepcidin-25 (β = 0.44) were found to be the determinants of CIMT in hemodialysis patients. Our results implicate that hepcidin may take part in pathophysiology of atherosclerosis and cardiovascular disease in hemodialysis patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".