Clinical Value of Complement Activation Biomarkers in Overt Diabetic Nephropathy
Bibliographic record
Abstract
Background Experimental studies support a role of complement activation in diabetic nephropathy (DN), yet few clinical correlates exist. We evaluated urinary levels of sC5b-9 membrane attack complex (MAC) in patients with overt DN, and examined its association with the glomerular filtration rate (GFR) decline, proteinuria, and inflammatory biomarkers. We explored different complement pathways and compared our findings to autoimmune glomerulonephritis. Methods We prospectively followed 83 patients with DN and obtained repeated measurements of proteinuria, complement fragments (sC5b-9, C4a, C1q, mannose-binding lectin–associated serine protease [MASP]-1, and factor Bb), monocyte chemoattractant protein-1 (MCP-1), and transforming growth factor (TGF)-β1. We assessed independence and interactions using general linear models and repeated measures analyses and compared levels with subjects with active focal and segmental glomerulosclerosis, ANCA-associated vasculitis, and membranous and IgA nephropathies ( n = 63). Results The diabetic cohort had an initial GFR of 25 ± 9 ml/min per 1.73 m 2 and a renal function decline of 2.9 ± 3.0 ml/min per 1.73 m 2 per year. All complement biomarkers were strongly intercorrelated and associated with biomarker inflammation and fibrosis, proteinuria, and the rate of renal function decline. There was a significant interaction ( P = 0.03) between the level of proteinuria and urinary sC5b-9: in individuals with higher levels of urinary MAC, the relationship between proteinuria and the rate of renal function decline was more pronounced than in those with low urinary MAC. Finally, patients with DN had levels of urinary sC5b-9 comparable to autoimmune glomerulonephritis, when stratified by the level of proteinuria. Conclusion Urinary MAC is present in patients with overt DN at levels comparable to autoimmune glomerulonephritis and correlates with the GFR decline, supporting that complement activation and its measurement are clinically relevant in DN.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".