Elevated urinary CRELD2 is associated with endoplasmic reticulum stress–mediated kidney disease
Bibliographic record
Abstract
ER stress has emerged as a signaling platform underlying the pathogenesis of various kidney diseases. Thus, there is an urgent need to develop ER stress biomarkers in the incipient stages of ER stress-mediated kidney disease, when a kidney biopsy is not yet clinically indicated, for early therapeutic intervention. Cysteine-rich with EGF-like domains 2 (CRELD2) is a newly identified protein that is induced and secreted under ER stress. For the first time to our knowledge, we demonstrate that CRELD2 can serve as a sensitive urinary biomarker for detecting ER stress in podocytes or renal tubular cells in murine models of podocyte ER stress-induced nephrotic syndrome and tunicamycin- or ischemia-reperfusion-induced acute kidney injury (AKI), respectively. Most importantly, urinary CRELD2 elevation occurs in patients with autosomal dominant tubulointerstitial kidney disease caused by UMOD mutations, a prototypical tubular ER stress disease. In addition, in pediatric patients undergoing cardiac surgery, detectable urine levels of CRELD2 within postoperative 6 hours strongly associate with severe AKI after surgery. In conclusion, our study has identified CRELD2 as a potentially novel urinary ER stress biomarker with potential utility in early diagnosis, risk stratification, treatment response monitoring, and directing of ER-targeted therapies in selected patient subgroups in the emerging era of precision nephrology.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".