Acute-Phase Proteins and Iron Status in Cats with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: The role of inflammation in the development and progression of chronic kidney disease (CKD) in cats is not well characterized. Hepcidin is a recently discovered acute-phase protein (APP) that plays an important role in iron metabolism and contributes to the development of anemia in humans with CKD. OBJECTIVES: To compare serum APP concentrations, iron status, and erythropoietin (EPO) concentrations in healthy cats and cats with naturally occurring CKD. ANIMALS: A total of 18 healthy control cats and 38 cats with CKD. METHODS: Prospective study. After complete physical examination and routine blood analysis, the following tests were performed: serum amyloid A (SAA), haptoglobin (HAP), EPO, serum iron and ferritin concentration as well as total iron-binding capacity (TIBC). Serum hepcidin-25 concentration was measured by ELISA kit designed for use in humans. RESULTS: Mean SAA and hepcidin concentrations were significantly higher and mean total iron and TIBC were significantly lower in the CKD group (P < .05). There was a significant positive correlation between serum creatinine concentration (CRT) and 2 of the APPs (SAA and hepcidin; P < .05). Increases in SAA and hepcidin were associated with decreases in TIBC and hematocrit in the CKD group. Fourteen (37%) of the cats with CKD were anemic, and these cats had significantly lower TIBC (P < .05), suggesting a functional iron deficiency. There was no association between survival time and APP, iron status, or EPO concentrations. CONCLUSIONS: Our data suggest that CKD in cats is associated with systemic inflammation and altered iron metabolism. With further validation in cats, hepcidin assays may help better characterize these relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".