Is Hypomagnesemia a Novel Risk Factor for New-Onset Diabetes Mellitus After Kidney Transplantation?
Bibliographic record
Abstract
Background: New-onset diabetes after transplantation (NODAT) increases the risk of cardiovascular events and graft failure among kidney transplant recipients. Some studies have suggested a link between hypomagnesaemia (HypoMg) and NODAT but this association remains controversial. Methods: A cohort study was conducted in 948 non-diabetic patients who received a kidney transplant from 1 Jan 2000 to 31 Dec 2011 (with follow-up to 30 Jun 2012). HypoMg was defined as a serum magnesium (Mg) < 0.74 mmol/L. NODAT was diagnosed based on the American Diabetes Association criteria. Cox proportional hazards models were fitted to examine the association of baseline Mg (at 1-month), time-varying Mg (every 3-months), and rolling average Mg (during 1-, 2-, or 3-month windows) with NODAT, while adjusting for other covariates. Results: Over 4,005.2 person-years of follow-up (median follow-up 3.3 years), 206 NODAT events were observed. The Cox proportional hazards models suggested an inverse relation between baseline serum Mg and NODAT (hazard ratio [HR] 1.21 per 0.1 mmol/L decrease in Mg [95% CI: 1.04, 1.41], P = 0.01). Similar results were observed for rolling average Mg levels during the previous 1-month (HR 1.28 [95% CI: 1.09, 1.49]; P = 0.002), 2-months (HR 1.28 [95% CI: 1.08, 1.53]; P = 0.01) and 3-months (HR 1.25 [95% CI: 1.03, 1.52]; P = 0.03). Patients with HypoMg (vs. normal Mg) were consistently associated with a significant 50 to 80% increase in the relative hazard for developing NODAT in baseline and rolling average Cox proportional hazards models. Interestingly, conventional time-varying models (with changes in Mg measured at discrete time points over follow-up) did not show a significant association between HypoMg and NODAT. Conclusion: Our results suggest that HypoMg, measured longitudinally, is an independent risk factor for NODAT in kidney transplant recipients. The role of magnesium supplementation to reduce the risk of NODAT requires further study. DISCLOSURE:Kim, S.: Grant/Research Support, Astellas Pharma Canada, Novartis Pharma Canada, Genzyme Canada.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".