Serum osteoprotegerin is associated with pulse pressure in kidney transplant recipients
Bibliographic record
Abstract
Pulse pressure (PP) reflects increased large artery stiffness, which is caused, in part, by arterial calcification in patients with chronic kidney disease. PP has been shown to predict both cardiovascular and cerebrovascular events in various patient populations, including kidney transplant (KTX) recipients. Osteoprotegerin (OPG) is a marker and regulator of arterial calcification, and it is related to cardiovascular survival in hemodialysis patients. Here we tested the hypothesis that OPG is associated with increased pulse pressure. We cross-sectionally analyzed the association between serum OPG and PP in a prevalent cohort of 969 KTX patients (mean age: 51 +/- --13 years, 57% male, 21% diabetics, mean eGFR 51 +/- 20 ml/min/1.73 m2). Independent associations were tested in a linear regression model adjusted for multiple covariables. PP was positively correlated with serum OPG (rho = 0.284, p < 0.001). Additionally, a positive correlation was seen between PP versus age (r = 0.358, p < 0.001), the Charlson Comorbidity Index (r = 0.232, p < 0.001), serum glucose (r = 0.172, p < 0.001), BMI (r = 0.133, p = 0.001) and serum cholesterol (r = 0.094, p = 0.003). PP was negatively correlated with serum Ca, albumin and eGFR. The association between PP and OPG remained significant after adjusting for multiple potentially relevant covariables (beta = 0.143, p < 0.001). We conclude that serum OPG is independently associated with pulse pressure in kidney transplant recipients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".