Design of the Magnetic Resonance Imaging Evaluation of Mineralocorticoid Receptor Antagonism in Diabetic Atherosclerosis (<scp>MAGMA</scp>) Trial
Bibliographic record
Abstract
Mineralocorticoid receptor (MR) activation plays an essential role in promoting inflammation, fibrosis, and target organ damage. Currently, no studies are investigating MR antagonism in patients with type 2 diabetes mellitus (T2DM) with chronic kidney disease, at high risk for cardiovascular complications, who are otherwise not candidates for MR antagonism by virtue of heart failure. Further, there is limited information on candidate therapies that may demonstrate differential benefit from this therapy. We hypothesized that MR antagonism may provide additional protection from atherosclerosis progression in higher-risk patients who otherwise may not be candidates for such a therapeutic approach. In this double-blind, randomized, placebo-controlled trial, subjects with T2DM with chronic kidney disease (≥ stage 3) will be randomized in a 1:1 manner to placebo or spironolactone (12.5 mg with eventual escalation to 25 mg daily over a 4-week period). The co-primary efficacy endpoint will be percentage change in total atheroma volume in thoracic aorta and left ventricular mass at 52 weeks in patients treated with spironolactone vs placebo. Secondary outcomes include 24-hour mean systolic blood pressure, central aortic blood pressure, and insulin resistance (HOMA-IR) at 6 weeks. A novel measure in the study will be changes in candidate miRNAs that regulate expression of NR3C2 (MR gene) as well as measuring monocyte/macrophage polarization in response to therapy with spironolactone. We envision that our strategy of simultaneously probing the effects of a drug combined with analysis of mechanisms of action and predictive response will likely provide key information with which to design event-based trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".