Associations between Serum Leptin Level and Bone Turnover in Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Obesity is associated with increased parathyroid hormone (PTH) in the general population and in patients with chronic kidney disease (CKD). A direct effect of adipose tissue on bone turnover through leptin production has been suggested, but such an association has not been explored in kidney transplant recipients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This study examined associations of serum leptin with PTH and with biomarkers of bone turnover (serum beta crosslaps [CTX, a marker of bone resorption] and osteocalcin [OC, a marker of bone formation]) in 978 kidney transplant recipients. Associations were examined in multivariable regression models. Path analyses were used to determine if the association of leptin with bone turnover is independent of PTH. RESULTS: Higher leptin levels were associated with higher PTH and lower vitamin D levels, and adjustment for vitamin D attenuated the association between leptin and PTH. However, higher leptin was also significantly associated with lower levels of the bone turnover markers: 1 SD higher leptin was associated with 0.13 lower log-OC (-0.17, -0.08, P < 0.001) and 0.030 lower log-CTX (-0.045, -0.016, P < 0.001) after multivariable adjustments. Path analysis indicated that the association of leptin with PTH was mostly mediated through vitamin D, and that the association between leptin and bone turnover was independent of PTH and vitamin D. CONCLUSIONS: Elevated leptin level is associated with lower bone turnover independent of its effects on serum PTH in kidney transplant recipients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".