Iron-Induced Hypophosphatemia: A Review of Pathophysiology, Drug Safety, and Pharmacogenomic Perspectives
Bibliographic record
Abstract
Intravenous (IV) iron therapy is a cornerstone in the management of iron deficiency anemia, yet its administration is associated with significant side effects, most notably hypophosphatemia. This adverse event is not a class effect but is disproportionately linked to specific formulations, particularly ferric carboxymaltose (FCM). This review synthesizes the current clinical evidence, elucidates the central pathophysiological role of fibroblast growth factor 23 (FGF23), and explores a potential pharmacogenomic basis for individual susceptibility. Clinical data from numerous randomized controlled trials and meta-analyses confirm that FCM induces hypophosphatemia in over 50% of patients, a rate far exceeding that of other IV iron preparations. The proposed underlying mechanism involves a “two-hit” process: pre-existing iron deficiency upregulates FGF23 gene transcription, while FCM administration is thought to inhibit the proteolytic cleavage of the FGF23 protein. This uncoupling of production and degradation leads to a surge in active, intact FGF23 (iFGF23), causing renal phosphate wasting. We explore the potential for genetic polymorphisms in FGF23 and its key processing enzymes, such as FURIN, GALNT3, and FAM20C, to modulate individual risk. Understanding this complex interplay is crucial for risk stratification, appropriate formulation selection, and patient monitoring to mitigate the acute and chronic consequences of iatrogenic hypophosphatemia, including debilitating fatigue and osteomalacia.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".