Defining subgroups of pediatric nephrotic patients with urine proteomics
Bibliographic record
Abstract
The molecular pathophysiology of nephrotic syndrome remains largely elusive in pediatric patients. While most children with minimal change disease (MCD) show favorable responses to immunosuppressive therapy, those with focal segmental glomerulosclerosis (FSGS) often exhibit poorer treatment responses, with many experiencing either partial remission or no remission of proteinuria. The need for reliable glomerular disease biomarkers to predict treatment response and understand molecular pathways governing responsiveness and resistance is a critical unmet need in pediatric nephrology. In this study, we sought to characterize urine proteomes in children with MCD and FSGS to identify biomarkers distinguishing disease activity and associated molecular pathways. Using quantitative proteomics, urine proteins from children with MCD and FSGS in the CureGN Study were identified and correlated with disease onset and activity. Unbiased cluster analyses of nephrotic urine proteomes demonstrated a cluster with relatively increased immune response and complement proteins, suggesting important distinctions in disease characteristics within the nephrotic subgroups. These analyses yielded patient subpopulations with proteinuria and distinct urine proteome differences associated with 116 proteins exerting cluster separation in the multivariate analyses. These findings highlight the potential of unsupervised clustering to identify disease subgroups and provide insights into the underlying molecular heterogeneity within nephrotic syndrome, paving the way for more tailored therapeutic strategies and improved patient management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".