Clinical Use of Complement, Inflammation, and Fibrosis Biomarkers in Autoimmune Glomerulonephritis
Bibliographic record
Abstract
INTRODUCTION: Complement activation, inflammation, and fibrosis play central roles in the mechanisms of injury in autoimmune glomerulonephritis (GN) but they are seldom assessed in epidemiologic studies. The measurement of urinary biomarkers of these pathways of injury could parallel disease activity and add clinical value beyond proteinuria. METHODS: We performed a prospective cohort study of 100 patients with focal and segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), IgA nephropathy (IgAN), lupus nephritis (LN), anti-neutrophil cytoplasmic autoantibody-associated vasculitis (AAV), and membranoproliferative GN (MPGN) followed for 33 (18-54) months. Repeated urinary samples were collected throughout their follow-up to determine proteinuria, urinary sC5b-9, monocyte chemoattractant protein-1 (MCP-1), and transforming growth factor-beta 1 (TGF-β1), expressed as creatinine ratios. We identified 177 periods of active and inactive disease based on current remission definitions for each disease. RESULTS: Urinary sC5b-9, MCP-1, and TGF-β1 were present in each disease. In periods leading to a remission, the reduction of urinary sC5b-9 was 91%, greater than for proteinuria with 76%. During inactive periods, those who did not experience a relapse maintained lower levels of biomarkers compared with those who relapsed. At that time, the increase in urinary sC5b-9 was significantly greater than the rise in proteinuria (8.5-fold increase compared with 3.2-fold) and urinary MCP-1 and TGF-β1. Using current remission definitions for each disease, thresholds for each biomarker were determined using receiver operating characteristic curves. Individuals who averaged levels below these cutoffs during their follow-up had better renal outcomes. CONCLUSION: In autoimmune glomerular diseases, urinary sC5b-9, MCP-1, and TGF-β1 are present and parallel disease activity and outcomes. Urinary sC5b-9 appears to be a more discerning marker of immunologic remissions and relapses.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".