Differential diagnosis of vitamin D–related hypercalcemia using serum vitamin D metabolite profiling
Bibliographic record
Abstract
ABSTRACT Genetic causes of vitamin D–related hypercalcemia are known to involve mutation of 25-hydroxyvitamin D-24-hydroxylase CYP24A1 or the sodium phosphate co-transporter SLC34A1, which result in excessive 1,25-(OH)2D hormonal action. However, at least 20% of idiopathic hypercalcemia (IH) cases remain unresolved. In this case-control study, we used precision vitamin D metabolite profiling based on liquid chromatography–tandem mass spectrometry (LC-MS/MS) of an expanded range of vitamin D metabolites to screen German and French cohorts of hypercalcemia patients, to identify patients with altered vitamin D metabolism where involvement of CYP24A1 or SLC34A1 mutation had been ruled out and who possessed normal 25-OH-D3:24,25-(OH)2D3 ratios. Profiles were compared to those of hypercalcemia patients with hypervitaminosis D, Williams-Beuren syndrome (WBS), CYP24A1 mutation, and normal subjects with a range of 25-OH-D levels. We observed that certain IH and WBS patients exhibited a unique profile comprising eightfold to 10-fold higher serum 23,25,26-(OH)3D3 and 25-OH-D3-26,23-lactone than normals, as well as very low serum 1,25-(OH)2D3 (2–5 pg/ml) and elevated 1,24,25-(OH)3D3, which we interpret implies hypersensitive expression of vitamin D–dependent genes, including CYP24A1, as a general underlying mechanism of hypercalcemia in these patients. Because serum 25-OH-D3 and 24,25-(OH)2D3 remained normal, we excluded the possibility that the aberrant profile was caused by hypervitaminosis D, but instead points to an underlying genetic cause that parallels the effect of Williams syndrome transcription factor deficiency in WBS. Furthermore, we observed normalization of serum calcium and vitamin D metabolite profiles at follow-up of an IH patient where 25-OH-D was reduced to 9 ng/ml, suggesting that symptomatic IH may depend on vitamin D nutritional status. Other hypercalcemic patients with complex conditions exhibited distinct vitamin D metabolite profiles. Our work points to the importance of serum vitamin D metabolite profiling in the differential diagnosis of vitamin D–related hypercalcemia that can rationalize expensive genetic testing, and assist healthcare providers in selecting appropriate treatment. © 2021 American Society for Bone and Mineral Research (ASBMR).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".