Biomarkers of inflammation and repair in kidney disease progression
Bibliographic record
Abstract
INTRODUCTIONAcute kidney injury and chronic kidney disease (CKD) are common in hospitalized patients. To inform clinical decision making, more accurate information regarding risk of long-term progression to kidney failure is required.METHODSWe enrolled 1538 hospitalized patients in a multicenter, prospective cohort study. Monocyte chemoattractant protein 1 (MCP-1/CCL2), uromodulin (UMOD), and YKL-40 (CHI3L1) were measured in urine samples collected during outpatient follow-up at 3 months. We followed patients for a median of 4.3 years and assessed the relationship between biomarker levels and changes in estimated glomerular filtration rate (eGFR) over time and the development of a composite kidney outcome (CKD incidence, CKD progression, or end-stage renal disease). We paired these clinical studies with investigations in mouse models of renal atrophy and renal repair to further understand the molecular basis of these markers in kidney disease progression.RESULTSHigher MCP-1 and YKL-40 levels were associated with greater eGFR decline and increased incidence of the composite renal outcome, whereas higher UMOD levels were associated with smaller eGFR declines and decreased incidence of the composite kidney outcome. A multimarker score increased prognostic accuracy and reclassification compared with traditional clinical variables alone. The mouse model of renal atrophy showed greater Ccl2 and Chi3l1 mRNA expression in infiltrating macrophages and neutrophils, respectively, and evidence of progressive renal fibrosis compared with the repair model. The repair model showed greater Umod expression in the loop of Henle and correspondingly less fibrosis.CONCLUSIONSBiomarker levels at 3 months after hospitalization identify patients at risk for kidney disease progression.FUNDINGNIH.
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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 | 0.002 | 0.024 |
| 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) | 0.000 | 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 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".