Circulating cathepsin-S levels correlate with GFR decline and sTNFR1 and sTNFR2 levels in mice and humans
Bibliographic record
Abstract
Cardiovascular complications determine morbidity/mortality in chronic kidney disease (CKD). We hypothesized that progressive CKD drives the release of cathepsin-S (Cat-S), a cysteine protease that promotes endothelial dysfunction and cardiovascular complications. Therefore, Cat-S, soluble tumor-necrosis-factor receptor (sTNFR) 1/2 and glomerular filtration rate (GFR) were measured in a CKD mouse model, a German CKD-cohort (MCKD, n = 421) and two Swedish community-based cohorts (ULSAM, n = 764 and PIVUS, n = 804). Association between Cat-S and sTNFR1/2/GFR was assessed using multivariable linear regression. In the mouse model, Cat-S and sTNFR1/2 concentrations were increased following the progressive decline of GFR, showing a strong correlation between Cat-S and GFR (r = -0.746, p < 0.001) and Cat-S and sTNFR1/sTNFR2 (r = 0.837/0.916, p < 0.001, respectively). In the human cohorts, an increase of one standard deviation of estimated GFR was associated with a decrease of 1.008 ng/ml (95%-confidence interval (95%-CI) -1.576-(-0.439), p < 0.001) in Cat-S levels in MCKD; in ULSAM and PIVUS, results were similar. In all three cohorts, Cat-S and sTNFR1/sTNFR2 levels were associated in multivariable linear regression (p < 0.001). In conclusion, as GFR declines Cat-S and markers of inflammation-related endothelial dysfunction increase. The present data indicating that Cat-S activity increases with CKD progression suggest that Cat-S might be a therapeutic target to prevent cardiovascular complications in CKD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".