The Role of Angiopoietins in Cardiovascular Outcomes of Kidney Transplant Recipients: An Ancillary Study from the FAVORIT
Bibliographic record
Abstract
INTRODUCTION: Kidney transplant recipients (KTRs) have increased risk of cardiovascular disease (CVD) mortality. We investigated vascular biomarkers, angiopoietin-1, and angiopoietin-2 (angpt-1, -2), in CVD development in KTRs. METHODS: This ancillary study from the FAVORIT evaluates the associations of baseline plasma angpt-1, -2 levels in CVD development (primary outcome) and graft failure (GF) and death (secondary outcomes) in 2000 deceased donor KTRs. We used Cox regression to analyze the association of biomarker quartiles with outcomes. We adjusted for demographic; CVD- and transplant-related variables; medications; urine albumin-to-creatinine ratio; and randomization status. We calculated areas under the curves (AUCs) to predict CVD or death, and GF or death by incorporating biomarkers alongside clinical variables. RESULTS: Participants' median age was 52 IQR [45, 59] years: with 37% women and 73% identifying as white. Median time from transplantation was 3.99 IQR [1.58, 7.93] years and to CVD development was 2.54 IQR [1.11-3.80] years. Quartiles of angpt-1 were not associated with outcomes. Whereas higher levels of angpt-2 (quartile 4) were associated with about 2 times the risk of CVD, GF, and death (aHR 1.85 [1.25-2.73], p < 0.01; 2.24 [1.36-3.70)], p < 0.01; 2.30 [1.48-3.58], p < 0.01, respectively) as compared to quartile 1. Adding angiopoietins to preexisting clinical variables improved prediction of CVD or death (AUC improved from 0.70 to 0.72, p = 0.005) and GF or death (AUC improved from 0.68 to 0.70, p = 0.005). Angpt-2 may partially explain the increased risk of future CVD in KTRs. Further research is needed to assess the utility of using angiopoietins in the clinical care of KTRs. CONCLUSION: Angpt-2 may be a useful prognostic tool for future CVD in KTRs. Combining angiopoietins with clinical markers may tailor follow-up to mitigate CVD risk.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 0.001 |
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".