Urinary monocyte chemotactic protein 1: marker of renal function decline in diabetic and nondiabetic proteinuric renal disease
Bibliographic record
Abstract
BACKGROUND: Reliable biomarkers are needed to identify patients with glomerular disease at risk of progression. Transforming growth factor beta 1 (TGF-β1) and monocyte chemotactic protein 1 (MCP-1) play key roles in promoting renal tissue injury. Whether their urinary measurement adds value to current predictors of progression is uncertain. METHODS: We enrolled patients with diabetic (n=53) and nondiabetic (n=47) proteinuric renal disease and retrospectively studied their rate of renal function decline over a defined period of 2 years. We simultaneously measured urinary protein, MCP-1 and TGF-β1, standardized to urinary creatinine. RESULTS: The initial estimated glomerular filtration rate, proteinuria and rate of renal function decline (slope) were 36 ml/min per 1.73 m2, 1.1 g/day and -4.0 ± 7.2 ml/ min per 1.73 m2 year. Median urinary TGF-β1 and MCP- 1 levels were 0.3 (range 0.0-28.1) and 18 (range 3-370) ng/mmol of creatinine, respectively. Urinary protein and MCP-1 to creatinine ratios were associated with slope, and this applied to both diabetic and nondiabetic patients separately. Urinary TGF-β1 showed no relation to slope. However, the majority of its measurements were below the suggested reproducibility threshold. Using linear regression, both normalized urinary protein and MCP-1 were independently associated with the slope. Adding urinary MCP-1 to the model statistically raised the adjusted R2 from 0.35 to 0.40, refining patient risk stratification. Using cutoffs for urinary protein and MCP-1 obtained by receiver operating characteristic curves, the risk of progression was confidently determined in 80% of patients. CONCLUSION: Urinary MCP-1 is a marker of renal function decline in diabetic and nondiabetic proteinuric renal disease, independent of and additive to proteinuria.
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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) | 0.001 | 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) | 0.000 | 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".