Longitudinal Proteinuria Trajectories and Their Association With Kidney Failure in Minimal Change Disease and Focal Segmental Glomerulosclerosis: A CureGN Study
Bibliographic record
Abstract
Background: Proteinuria often guides treatment decisions and measures response in glomerular disease. We characterized longitudinal proteinuria trajectories in patients with MCD and FSGS and assessed associations with kidney failure (KF). Methods: Participants with MCD and FSGS enrolled in the Cure Glomerulonephropathy (CureGN) study with a first diagnostic kidney biopsy in the 5 years prior to enrollment and ≥2 years of follow-up were included. Participants were grouped based on proteinuria trajectory in the first 2 years post-enrollment using latent class trajectory analysis. Associations between group membership and incidence of KF beyond 2 years post enrollment were assessed using multivariable Cox regression. Results: 887 participants (423 MCD, 464 FSGS) were included. Median age was 17 years (IQR 8-44); 53% were male; 22% were Black. Median follow-up time from enrollment was 4.5 years (IQR 3.4-5.7). Mean (SD) eGFR (ml/min/1.73m2) and UPCR (g/g) at enrollment were 89.3 (33.0) and 2.5 (3.2), respectively. Three groups were identified (Figure); Group 1 (78%) had consistently low UPCR (<1); Group 2 (12%) had high UPCR at enrollment that decreased by the start of year 2 to <2; Group 3 (10%) had consistently high UPCR (>6). Groups 1 and 2 were approximately evenly split between MCD and FSGS (48% and 52% for Group 1, 56% and 44% for Group 2), while Group 3 was predominantly FSGS (65%). Group 3 had higher hazard of progression to KF after 2 years post-enrollment compared to Group 1 (HR=3.4, 95% CI=1.6-7.3), after adjusting for diagnosis, age, eGFR at enrollment, and years from biopsy to enrollment. No interaction between diagnosis and proteinuria trajectory was detected (p=0.24). Conclusions: Longitudinal proteinuria trajectories add additional information beyond diagnosis and baseline disease severity when characterizing risk of KF in patients with MCD and FSGS. Funding: NIDDK SupportMean UPCR trajectories with 95% confidence intervals.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".