Circulating Angiopoietin-2 levels predict mortality in kidney transplant recipients: a 4-year prospective case-cohort study
Bibliographic record
Abstract
Angiopoietin 2 (Angpt2) impairs endothelial function by preventing angiopoietin 1 from binding to their common endothelial-specific receptor Tie2. Here, we examined whether circulating Angpt2 predicts outcome in kidney transplant recipients. For this case-cohort study, we selected 130 kidney transplant recipients who had died or returned to dialysis within the first 2 years of follow-up of our cohort study, as well as 130 age- and gender-matched kidney transplant recipients without an event (controls) from a total of 993 kidney transplant recipients. The total of 260 selected patients were followed in median 4 years. Serum Angpt2 at baseline was measured using an in-house immunoluminometric assay. Median Angpt2 concentrations were significantly higher in patients who died [median (interquartile range--IQR) 3.6 (2.8-5.9) ng/ml] as compared to patients who did not die during the study period [2.8 (2.1-4.1) ng/ml; P < 0.001]. Ln (natural log) Angpt2 levels correlated positively with C-reactive protein levels (r = 0.315, P < 0.001) and the Charlson Comorbidity Index (r = 0.188, P = 0.002) and were inversely associated with eGFR (r = -0.301, P < 0.001) hemoglobin (r = -0.269, P < 0.001), and serum albumin concentrations (r = -0.382, P < 0.001). On multivariate analyses, baseline Angpt2 levels independently predicted all-cause mortality (multivariable-adjusted hazard ratio associated with one natural log unit higher Angpt2 level: 1.70 (95% confidence interval: 1.10-2.61)). In our analysis, circulating Angpt2 was an independent predictor of all-cause mortality in stable, prevalent kidney transplant recipients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".