Severe Hypomagnesemia Due to Renal Magnesium Wasting in Patient with HNF1B Mutation and Pseudogout
Bibliographic record
Abstract
Introduction: Hepatocyte nuclear factor 1β (HNF1B) is a transcription factor that is essential for proper development of the kidney and pancreas. Here, we report a case of severe hypomagnesemia and cystic kidney disease due to HNF1B mutation in a patient with pseudogout. Case Description: A 41-year-old female with hypothyroidism and frequently flaring pseudogout with severe chondrocalcinosis in multiple joints was referred to nephrology for assessment of hypomagnesemia refractory to oral magnesium (Mg) supplementation. There was no history of gastrointestinal losses, offending medications, or family history of hypomagnesemia. Plasma Mg was 0.35 mmol/L and she had metabolic alkalosis with total CO2 of 32 mmol/L but normal plasma calcium, potassium, sodium, phosphate, creatinine, and parathyroid hormone. Plasma Mg was very low for at least one year prior to nephrology assessment. Urine fractional excretion of Mg (FEMg) was 3.9%, consistent with renal Mg wasting. Genetic testing for hereditary causes of renal Mg wasting revealed a heterozygous pathogenic HNF1B gene variant, identified as 1.26 Mb deletion, seq[GRCh37] del(17)(q12),chr17:g.34842466_36104935del. An abdominal CT scan revealed a congenital agenesis variant of the dorsal pancreas and multiple left renal cysts, both in keeping with her HNF1B mutation. She was treated with amiloride, titrated to 15 mg BID, to reduce renal Mg wasting, in addition to oral Mg glycinate supplementation, but her Mg continued to remain low. Consequently, she was also started on IV Mg sulfate 5 mg weekly. Plasma Mg improved slightly to 0.5 mmol/L but FEMg remained high at 5.5%. Dapagliflozin 10 mg daily was further added and FEMg improved to 2.6%. Her plasma Mg fluctuates depending on timing relative to weekly IV Mg infusion but has been as normal as 0.9 mmol/L. She continues on oral Mg supplementation, weekly IV Mg infusions, and amiloride and dapagliflozin. Discussion: Severe hypomagnesemia is known to cause increased chondrocalcinosis likely contributing to the patient’s poorly controlled pseudogout. Numerous renal manifestations are associated with HNF1B mutations including cystic kidney disease and renal Mg wasting as demonstrated in this case. Hypomagnesemia when associated with HNF1B variants may be severe, refractory to oral Mg supplementation, and consequently may benefit from IV Mg infusions and diuretic therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 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".