Serum Adiponectin Levels and Mortality after Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Adiponectin (ADPN), an adipose tissue-derived hormone, has protective properties with respect to atherogenesis, inflammation, and energy homeostasis. Its beneficial role has not been consistent in patients with CKD or those undergoing dialysis. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This study examined the association of plasma ADPN levels in 987 prevalent kidney transplant recipients (mean age ± SD, 51.0±12.8 years; estimated GFR, 52.8±21.9 ml/min per 1.73 m(2); median time since transplant, 78 months) on all-cause mortality and death-censored graft failure. Patients were enrolled between February and August 2007 and were followed for a median of 51 months (interquartile range, 49-53 months). Using Cox proportional hazard models, the association of log-transformed plasma adiponectin was studied, with and without adjustment for demographic variables, baseline GFR, markers of inflammation, and cardiovascular risk factors. RESULTS: At baseline, patients in the lowest ADPN tertile were significantly more likely to be male; to be smokers; to have a higher baseline GFR, lower systolic BP, and lower HDL cholesterol level; and to have higher body mass index, abdominal circumference, C-reactive protein level, and total cholesterol level. The adjusted hazard ratio for death with elevated plasma ADPN (per natural log) was 1.44, and there was no significant interaction with any relevant cardiovascular risk subgroups (i.e., advanced age; diabetes; or elevated body mass index, waist circumference, C-reactive protein, or Framingham risk score). The hazard for death-censored graft failure was nonsignificant at 1.03. CONCLUSION: Elevated ADPN levels are associated with higher risk for death but not allograft failure in prevalent 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 | 0.001 | 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".