Urine Measurements of the Renin-Angiotensin System-Regulated Proteins Predict Death and Graft Loss in Kidney Transplant Recipients Enrolled in a Ramipril versus Placebo Randomized Controlled Trial
Bibliographic record
Abstract
The renin-angiotensin system (RAS) is involved in kidney fibrosis. We previously identified six RAS-regulated proteins (RHOB, BST1, LYPA1, GLNA, TSP1, and LAMB2) that were increased in the urine of patients with kidney allograft fibrosis, compared to patients without fibrosis. We hypothesized that these urinary RAS-regulated proteins predicted primary outcomes in kidney transplant recipients enrolled in the largest RAS inhibitor randomized controlled trial. Urine excretion of 10 peptides corresponding to the six RAS-regulated proteins was quantified using parallel reaction monitoring mass spectrometry assays (normalized by urine creatinine) in a subset of patients in the trial. Machine learning models predicting outcomes based on urine peptide excretion rates were developed and evaluated. Urine samples ( n = 111) from 56 patients were collected at 0, 6, 12, and 24 months. Twenty-four primary outcomes (doubling of serum creatinine, graft loss, or death) occurred in 17 patients. Logistic regression utilizing eight peptides of TSP1, BST1, LAMB2, LYPA1, and RHOB, from the last urine sample prior to outcomes, predicted a graft loss with an AUC of 0.78 ( p = 0.00001). A random forest classifier utilizing BST1 and LYPA1 peptides predicted death with an AUC of 0.80 ( p = 0.0016). Urine measurements of RAS-regulated proteins may predict outcomes in kidney transplant recipients, although further prospective studies are required.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".