Iron deficiency drives an autosomal dominant hypophosphatemic rickets (ADHR) phenotype in fibroblast growth factor-23 (Fgf23) knock-in mice
Bibliographic record
Abstract
Autosomal dominant hypophosphatemic rickets (ADHR) is unique among the disorders involving Fibroblast growth factor 23 (FGF23) because individuals with R176Q/W and R179Q/W mutations in the FGF23 (176)RXXR(179)/S(180) proteolytic cleavage motif can cycle from unaffected status to delayed onset of disease. This onset may occur in physiological states associated with iron deficiency, including puberty and pregnancy. To test the role of iron status in development of the ADHR phenotype, WT and R176Q-Fgf23 knock-in (ADHR) mice were placed on control or low-iron diets. Both the WT and ADHR mice receiving low-iron diet had significantly elevated bone Fgf23 mRNA. WT mice on a low-iron diet maintained normal serum intact Fgf23 and phosphate metabolism, with elevated serum C-terminal Fgf23 fragments. In contrast, the ADHR mice on the low-iron diet had elevated intact and C-terminal Fgf23 with hypophosphatemic osteomalacia. We used in vitro iron chelation to isolate the effects of iron deficiency on Fgf23 expression. We found that iron chelation in vitro resulted in a significant increase in Fgf23 mRNA that was dependent upon Mapk. Thus, unlike other syndromes of elevated FGF23, our findings support the concept that late-onset ADHR is the product of gene-environment interactions whereby the combined presence of an Fgf23-stabilizing mutation and iron deficiency can lead to ADHR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".