Detailed Quantification of Glomerular Structural Lesions Associates with Clinical Outcomes and Transcriptomic Profiles in Nephrotic Syndrome
Bibliographic record
Abstract
ABSTRACT The current classification system for focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) does not fully capture the complex structural changes in kidney biopsies, nor the clinical and molecular heterogeneity of these diseases. The Nephrotic Syndrome Study Network (NEPTUNE) Digital Pathology Scoring System (NDPSS) was applied to 221 NEPTUNE FSGS/MCD digital kidney biopsies for glomerular scoring using 37 descriptors. The descriptor-based glomerular profiles were used to cluster patients with similar morphologic characteristics. Glomerular descriptors and patient clusters were assessed for association with time to proteinuria remission and disease progression by using adjusted Cox models, and eGFR measures over time by using linear mixed models. Messenger RNA from glomerular tissue was used to assess differentially expressed genes (DEG) between clusters and identify genes associated with individual descriptors driving cluster membership. Three clusters were identified: X (N=56), Y (N=68), and Z (N=97). Clusters Y and Z had higher probabilities of proteinuria remission (HR [95% CI]= 1.95 [0.99, 3.85] and 3.29 [1.52, 7.13], respectively), lower hazards of disease progression 0.22 [0.08, 0.57] and 0.11 [0.03, 0.45], respectively), and greater loss of eGFR over time compared with X. Cluster X had 1920 DEGs compared to Y+Z, which reflected activation of pathways of immune response and inflammation. Six individual descriptors driving the clusters individually correlated with clinical outcomes and gene expression. The NDPSS allows for characterization of FSGS/MCD patients into clinically and biologically relevant categories and uncovers histologic parameters associated with clinical outcomes and molecular signatures not included in current classification systems. TRANSLATIONAL STATEMENT FSGS and MCD are heterogeneous diseases that manifest with a variety of structural changes often not captured by conventional classification systems. This study shows that a detailed morphologic analysis and quantification of these changes allows for better representation of the structural abnormalities within each patient and for grouping patients with similar morphologic profiles into categories that are clinically and biologically relevant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".