Effects of dietary iron intake and chronic kidney disease on fibroblast growth factor 23 metabolism in wild-type and hepcidin knockout mice
Bibliographic record
Abstract
In the setting of normal kidney function, iron deficiency is associated with increased FGF23 production and cleavage, altering circulating FGF23 levels. Our objective was to determine how chronic kidney disease (CKD) and dietary iron intake affect FGF23 production and metabolism in wild-type (WT) and hepcidin knockout (HKO) mice. For 8 wk, the mice were fed diets that contained adenine (to induce CKD) or no adenine (control group), with either low-iron (4 ppm) or standard-iron (335 ppm) concentrations. The low-iron diet induced iron deficiency anemia in both the WT and HKO mice. Among the WT mice, in both the control and CKD groups, a low-iron compared with a standard-iron diet increased bone Fgf23 mRNA expression, C-terminal FGF23 (cFGF23) levels, and FGF23 cleavage as manifested by a lower percentage intact FGF23 (iFGF23). Independent of iron status, CKD was associated with inhibition of FGF23 cleavage. Similar results were observed in the HKO control and CKD groups. Dietary iron content was more influential on FGF23 parameters than the presence or absence of hepcidin. In the CKD mice (WT and HKO, total n = 42), independent of the effects of serum phosphate, iron deficiency was associated with increased FGF23 production but also greater cleavage, whereas worse kidney function was associated with increased FGF23 production but decreased cleavage. Therefore, in both the WT and HKO mouse models, dietary iron content and CKD affected FGF23 production and metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".