Maternal Hypercalcemia Due to Failure of 1,25-Dihydroxyvitamin-D<sub>3</sub>Catabolism in a Patient With<i>CYP24A1</i>Mutations
Bibliographic record
Abstract
CONTEXT: Calcium metabolism changes in pregnancy and lactation to meet fetal needs, with increases in 1,25-dihydroxyvitamin D [1,25-(OH)2D] during pregnancy playing an important role. However, these changes rarely cause maternal hypercalcemia. When maternal hypercalcemia occurs, further investigation is essential, and disorders of 1,25-(OH)2D catabolism should be carefully considered in the differential diagnosis. CASE: A patient with a childhood history of recurrent renal stone disease and hypercalciuria presented with recurrent hypercalcemia and elevated 1,25-(OH)2D levels during pregnancy. Laboratory tests in the fourth pregnancy showed suppressed PTH, elevated 1,25-(OH)2D, and high-normal 25-hydroxyvitamin D levels, suggesting disordered vitamin D metabolism. Analysis revealed low 24,25-dihydroxyvitamin D3 and high 25-hydroxyvitamin D3 levels, suggesting loss of function of CYP24A1 (25-hydroxyvitamin-D3-24-hydroxylase). Gene sequencing confirmed that she was a compound heterozygote with the E143del and R396W mutations in CYP24A1. CONCLUSIONS: This case broadens presentations of CYP24A1 mutations and hypercalcemia in pregnancy. Furthermore, it illustrates that patients with CYP24A1 mutations can maintain normal calcium levels during the steady state but can develop hypercalcemia when challenged, such as in pregnancy when 1,25-(OH)2D levels are physiologically elevated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".