MMP-10 is Increased in Early Stage Diabetic Kidney Disease and can be Reduced by Renin-Angiotensin System Blockade
Bibliographic record
Abstract
Matrix metalloproteinases have been implicated in diabetic microvascular complications. However, little is known about the pathophysiological links between MMP-10 and the renin-angiotensin system (RAS) in diabetic kidney disease (DKD). We tested the hypothesis that MMP-10 may be up-regulated in early stage DKD, and could be down-regulated by angiotensin II receptor blockade (telmisartan). Serum MMP-10 and TIMP-1 levels were measured in 268 type 2 diabetic subjects and 111 controls. Furthermore, histological and molecular analyses were performed to evaluate the renal expression of Mmp10 and Timp1 in a murine model of early type 2 DKD (db/db) after telmisartan treatment. MMP-10 (473 ± 274 pg/ml vs. 332 ± 151; p = 0.02) and TIMP-1 (573 ± 296 ng/ml vs. 375 ± 317; p < 0.001) levels were significantly increased in diabetic patients as compared to controls. An early increase in MMP-10 and TIMP-1 was observed and a further progressive elevation was found as DKD progressed to end-stage renal disease. Diabetic mice had 4-fold greater glomerular Mmp10 expression and significant albuminuria compared to wild-type, which was prevented by telmisartan. MMP-10 and TIMP-1 are increased from the early stages of type 2 diabetes. Prevention of MMP-10 upregulation observed in diabetic mice could be another protective mechanism of RAS blockade in DKD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".