Vitamin D Levels and the Risk of Posttransplant Diabetes Mellitus After Kidney Transplantation
Bibliographic record
Abstract
INTRODUCTION: Given the burden of posttransplant diabetes mellitus and the high prevalence of low vitamin D levels in kidney transplant recipients, it is reasonable to consider vitamin D as a novel and potentially modifiable risk factor in this patient population. RESEARCH QUESTION: To determine the association between 25- hydroxyvitamin D (25(OH)D) level and posttransplant diabetes among kidney transplant recipients. Design: In a multi-center cohort study of 442 patients who received a kidney transplant between January 1, 2005 and December 31, 2010, serum samples within one-year before transplant were analyzed for 25(OH)D levels. The association between 25(OH)D and posttransplant diabetes were examined in Cox proportional hazard models. RESULTS: The median 25(OH)D level was 66 nmol/L. The cumulative probability of diabetes at 12-months by quartiles of 25(OH)D (< 42, 42 to 64.9, 65 to 94.9, and > 95 nmol/L) were 23.4%, 26.9%, 21.4%, and 15.6%, respectively. Compared to the highest 25(OH)D quartile, hazard ratios (95% CI) for the risk were 1.85 (1.03, 3.32), 2.01 (1.12, 3.60), 1.77 (0.96, 3.25) across the first to third quartiles, respectively. The associations were accentuated in a model restricted to patients on tacrolimus. When modeled as a continuous variable, 25(OH)D levels were significantly associated with a higher risk of diabetes (hazard ratio 1.06, 95% CI: 1.01, 1.13 per 10 nmol/L decrease). DISCUSSION: Serum 25(OH)D was an independent predictor of posttransplant diabetes in kidney transplant recipients. These results may inform the design of trials using vitamin D to reduce the risk in 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 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) | 0.000 | 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 | 0.000 |
| 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".