Atorvastatin lowers serum calcium levels in lithium-users: results from a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Although lithium is considered the gold-standard treatment for bipolar disorder (BD), it is associated with a variety of major endocrine and metabolic side effects, including parathyroid hormone (PTH) dependent hypercalcemia. Aside from surgery and medication discontinuation, there are limited treatments for hypercalcemia. This paper will assess data from a randomized controlled trial (RCT). METHODS: This is a secondary analysis of an RCT that explored the effects of atorvastatin (n = 27) versus placebo (n = 33) on lithium-induced nephrogenic diabetes insipidus (NDI) in patients with BD and major depressive disorder (MDD) using lithium (n = 60), over a 12-week period. This secondary analysis will explore serum calcium levels and thyroid stimulating hormone (TSH) measured at baseline, week 4, and week 12. RESULTS: At 12-weeks follow-up while adjusting results for baseline, linear regression analyses found that corrected serum calcium levels were significantly lower in the treatment group (mean (M) = 2.30 mmol/L, standard deviation (SD) = 0.07) compared to the placebo group (M = 2.33 mmol/L, SD = 0.07) (β = - 0.03 (95% C.I.; - 0.0662, - 0.0035), p = 0.03) for lithium users. There were no significant changes in TSH. CONCLUSION: In lithium users with relatively normal calcium levels, receiving atorvastatin was associated with a decrease in serum calcium levels. Although exciting, this is a preliminary finding that needs further investigation with hypercalcemic patients. Future RCTs could examine whether atorvastatin can treat PTH dependent hypercalcemia due to lithium and other causes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".