Endostatin Level is Associated with Kidney Injury in the Elderly: Findings from Two Community-Based Cohorts
Bibliographic record
Abstract
BACKGROUND: We aimed to investigate the associations between circulating endostatin and the different aspects of renal dysfunction, namely, estimated (cystatin C) glomerular filtration rate (GFR) and urine albumin-creatinine ratio (ACR). METHODS: Two independent longitudinal community-based cohorts of elderly. ULSAM, n = 786 men; age 78 years; median GFR 74 ml/min/1.73 m(2); median ACR 0.80 mg/mmol); and PIVUS, n = 815; age 75 years; 51% women; median GFR; 67 ml/min/1.73 m(2); median ACR 1.39 mg/mmol. Cross-sectional associations between the endostatin levels and GFR as well as ACR, and longitudinal association between endostatin at baseline and incident CKD (defined as GFR <60 ml/min/1.73 m(2)) were assessed. RESULTS: In cross-sectional regression analyses adjusting for age, gender, inflammation, and cardiovascular risk factors, serum endostatin was negatively associated with GFR (ULSAM: B-coefficient per SD increase -0.51, 95% CI (-0.57, -0.45), p < 0.001; PIVUS -0.47, 95% CI (-0.54, -0.41), p < 0.001) and positively associated with ACR (ULSAM: B-coefficient per SD increase 0.24, 95% CI (0.15, 0.32), p < 0.001; PIVUS 0.13, 95% CI (0.06-0.20), p < 0.001) in both cohorts. Moreover, in longitudinal multivariable analyses, higher endostatin levels were associated with increased risk for incident CKD defined as GFR <60 ml/min/1.73 m(2) at re-investigations in both ULSAM (odds ratio per SD increase of endostatin 1.39 (95% CI 1.01-1.90) and PIVUS 1.68 (95% CI 1.36-2.07)). CONCLUSIONS: Higher circulating endostatin is associated with lower GFR and higher albuminuria and independently predicts incident CKD in elderly subjects. Further studies are warranted to investigate the underlying mechanisms linking endostatin to kidney pathology, and to evaluate the clinical relevance of our findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".