Posttransplant Diabetes Mellitus and Immunosuppression Selection in Older and Obese Kidney Recipients
Bibliographic record
Abstract
Rationale & Objective Posttransplant diabetes mellitus (DM) after kidney transplantation increases morbidity and mortality, particularly in older and obese recipients. We aimed to examine the impact of immunosuppression selection on the risk of posttransplant DM among both older and obese kidney transplant recipients. Study Design Retrospective database study. Setting & Participants Kidney-only transplant recipients aged ≥18 years from 2005 to 2016 in the United States from US Renal Data System records, which integrate Organ Procurement and Transplantation Network/United Network for Organ Sharing records with Medicare billing claims. Exposures Various immunosuppression regimens in the first 3 months after transplant. Outcomes Development of DM >3 months-to-1 year posttransplant. Analytical Approach We used multivariable Cox regression to compare the incidence of posttransplant DM by immunosuppression regimen with the reference regimen of thymoglobulin (TMG) or alemtuzumab (ALEM) with tacrolimus + mycophenolic acid + prednisone using inverse propensity weighting. Results 12.7% of kidney transplant recipients developed posttransplant DM with higher incidences in older (≥55 years vs <55 years: 16.7% vs 10.1%) and obese (body mass index [BMI] ≥ 30 kg/m 2 vs BMI < 30 kg/m 2 : 17.1% vs 10.9%) patients. The incidence of posttransplant DM was lower with steroid avoidance [TMG/ALEM + no prednisone (8.4%) and IL2rAb + no prednisone (9.7%)] than TMG/ALEM with triple therapy (13.1%). After adjustment for donor and recipient characteristics, TMG/ALEM with steroid avoidance was beneficial for all groups [age < 55 years: adjusted HR (aHR), 0.63 (95% confidence interval [CI], 0.54-0.72); age ≥ 55 years: aHR, 0.69 (95% CI, 0.60-0.79); BMI < 30 kg/m 2 : aHR, 0.69 (95% CI, 0.60-0.78); BMI ≥ 30 kg/m 2 : aHR, 0.67 (95% CI, 0.57-0.79)]. However, IL2rAb with steroid avoidance was beneficial only for older patients (aHR, 0.76; 95% CI, 0.58-0.99) and for those with BMI < 30 kg/m 2 (aHR, 0.63; 95% CI, 0.46-0.87). Limitations Retrospective study and lacked data on immunosuppression levels. Conclusions The beneficial impact of steroid avoidance using tacrolimus on posttransplant DM appears to differ by patient age and induction regimen.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".