The effect of micro-particle curcumin on chronic kidney disease progression: the MPAC-CKD randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Curcumin is a commonly used herbal supplement with anti-inflammatory and anti-fibrotic properties. Animal studies and small human trials suggest that curcumin reduces albuminuria in patients with chronic kidney disease (CKD). Micro-particle curcumin is a new, more bioavailable formulation of curcumin. METHODS: To determine whether micro-particle curcumin versus placebo slows the progression of albuminuric CKD we conducted a randomized, double-blind, placebo-controlled trial with 6-month follow-up. We included adults with albuminuria [a random urine albumin-to-creatinine ratio >30 mg/mmol (265 mg/g) or a 24-h urine collection with more than 300 mg of protein] and an estimated glomerular filtration rate (eGFR) between 15 and 60 mL/min/1.73 m2 within the 3 months before randomization. We randomly allocated participants 1:1 to receive micro-particle curcumin capsules (90 mg/day) or matching placebo for 6 months. After randomization, the co-primary outcomes were the changes in albuminuria and the eGFR. RESULTS: We enrolled 533 participants, but 4/265 participants in the curcumin group and 15/268 in the placebo group withdrew consent or became ineligible. The 6-month change in albuminuria did not differ significantly between the curcumin and placebo groups [geometric mean ratio 0.94, 97.5% confidence interval (CI) 0.82 to 1.08, P = .32]. Similarly, the 6-month change in eGFR did not differ between groups (mean between-group difference -0.22 mL/min/1.73 m2, 97.5% CI -1.38 to 0.95, P = .68). CONCLUSIONS: Ninety milligrams of micro-particle curcumin daily did not slow the progression of albuminuric CKD over 6 months. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02369549.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".