A double‐blind, randomized, placebo‐controlled pilot trial of atorvastatin for nephrogenic diabetes insipidus in lithium users
Bibliographic record
Abstract
OBJECTIVE: Lithium remains an important treatment for mood disorders but is associated with kidney disease. Nephrogenic diabetes insipidus (NDI) is associated with up to 3-fold risk of incident chronic kidney disease among lithium users. There are limited randomized controlled trials (RCT) for treatments of lithium-induced NDI, and existing therapies can be poorly tolerated. Therefore, novel treatments are needed for lithium-induced NDI. METHOD: We conducted a 12-week double-blind pilot RCT to assess the feasibility and efficacy of 20 mg/d atorvastatin vs placebo in the treatment of NDI in chronic lithium users. Patients, recruited between September 2017 and October 2018, were aged 18 to 85, currently on a stable dose of lithium, and determined to have NDI. RESULTS: Urinary osmolality (UOsm) at 12 weeks adjusted for baseline was not statistically different between groups (+39.6 mOsm/kg [95% CI, -35.3, 114.5] in atorvastatin compared to placebo groups). Secondary outcomes of fluid intake and aquaporin-2 excretions at 12 weeks adjusted for baseline were -0.13 L [95% CI, -0.54, 0.28] and 98.68 [95% CI, -190.34, 387.70], respectively. A moderate effect size was observed for improvements in baseline UOsm by ≥100 mOsm/kg at 12 weeks in patients who received atorvastatin compared to placebo (38.45% (10/26) vs 22.58% (7/31); Cohen's d = 0.66). CONCLUSION: Among lithium users with NDI, atorvastatin 20 mg/d did not significantly improve urinary osmolality compared to placebo over a 12-week period. Larger confirmatory trials with longer follow-up periods may help to further assess the effects of statins on NDI, especially within patients with more severe NDI.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".