Association between Post-Transplant Vitamin D, Metabolic Syndrome, and Post-Transplant Diabetes Mellitus
Bibliographic record
Abstract
Key Points Metabolic syndrome components after kidney transplant are associated with a higher likelihood of post-transplant diabetes. This predictive association persists independently of other prediabetes markers, including fasting sugar and hemoglobin A1c. Higher vitamin D-25 levels blunt this association; in patients with high metabolic syndrome burden, vitamin D supplementation may be warranted. Background Associations between 25-hydroxyvitamin D (25(OH)D) deficiency and diabetes have been observed in the general population, but are less delineated in kidney transplant recipients (KTRs), especially in the context of highly prevalent metabolic syndrome (MetS) features in KTRs. We hypothesized that vitamin D deficiency may present greater risk in KTRs with greater burden of MetS features. Methods We retrospectively evaluated 1792 KTRs with no treated diabetes at transplant between 1998 and 2018. Vitamin D was measured at ≥3 months post-transplant. MetS features were defined by the National Cholesterol Education Program, Adult Treatment Panel III (NCEP-ATP-III) criteria. The primary outcome was treated post-transplant diabetes mellitus (PTDM) incidence. Results In 1792 nondiabetic KTRs followed for 10,956 patient-years, 237 patients developed PTDM. For KTRs meeting NCEP-ATP-III criteria with fourth-quartile 25(OH)D, there were 1.5 new diagnoses per 100 patient-years versus 4.2 events per 100 patient-years in KTRs with first-quartile 25(OH)D ( P < 0.001). In multivariate survival regression, vitamin D was, accounting for individual NCEP-ATP-III criteria, associated with PTDM (hazard ratio, 0.93 per 10 nmol/ml 25(OH)D, P = 0.007) independently of fasting blood sugar and hemoglobin A1c. In marginal effects analysis, MetS effect on PTDM increased as serum 25(OH)D levels decreased. Conclusions Our study suggests that decreased 25(OH)D is associated with increased PTDM, and this marginal effect worsens as KTRs have an increased burden of MetS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".