Hypomagnesemia and the Risk of New-Onset Diabetes Mellitus after Kidney Transplantation
Bibliographic record
Abstract
Several studies suggest a link between post-transplant hypomagnesemia and new-onset diabetes after transplantation (NODAT), but this relationship remains controversial. We conducted a retrospective cohort study of 948 nondiabetic kidney transplant recipients from January 1, 2000, to December 31, 2011, to examine the association between serum magnesium level and NODAT. Multivariable Cox proportional hazards models were fitted to evaluate the risk of NODAT as a function of baseline (at 1 month), time-varying (every 3 months), and rolling-average (i.e., mean for 3 months moving at 3-month intervals) serum magnesium levels while adjusting for potential confounders. A total of 182 NODAT events were observed over 2951.2 person-years of follow-up. Multivariable models showed an inverse relationship between baseline serum magnesium level and NODAT (hazard ratio [HR], 1.24 per 0.1 mmol/L decrease; 95% confidence interval [95% CI], 1.05 to 1.46; P=0.01). The association with the risk of NODAT persisted in conventional time-varying (HR, 1.32; 95% CI, 1.14 to 1.52; P<0.001) and rolling-average models (HR, 1.34; 95% CI, 1.13 to 1.57; P=0.001). Hypomagnesemia (serum magnesium <0.74 mmol/L) also significantly associated with increased risk of NODAT in baseline (HR, 1.58; 95% CI, 1.07 to 2.34; P=0.02), time-varying (HR, 1.78; 95% CI, 1.29 to 2.45; P<0.001), and rolling-average models (HR, 1.83; 95% CI, 1.30 to 2.57; P=0.001). Our results suggest that lower post-transplant serum magnesium level is an independent risk factor for NODAT in kidney transplant recipients. Interventions targeting serum magnesium to reduce the risk of NODAT should be evaluated.
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".