Reduced Incidence of New-Onset Diabetes Mellitus after Renal Transplantation with 3-Hydroxy-3-Methylglutaryl-Coenzyme A Reductase Inhibitors (Statins)
Bibliographic record
Abstract
Statins have anti-inflammatory effects, modify endothelial function and improve peripheral insulin resistance. We hypothesized that statins influence the development of new-onset diabetes mellitus in renal transplant recipients. The records of all previously non-diabetic adults who received an allograft in Toronto between January 1, 1999 and December 31, 2001 were reviewed with follow-up through December 31, 2002. All patients receiving cyclosporine or tacrolimus, mycophenolate mofetil and prednisone were included. New-onset diabetes was diagnosed by the Canadian Diabetic Association criteria: fasting plasma glucose > or =7.0 mmol/L or 2-h postprandial glucose > or =11.1 mmol/L on more than two occasions. Statin use prior to diabetes development was recorded along with other variables. Cox proportional hazards models analyzing statin use as a time-dependent covariate were performed. Three hundred fourteen recipients met study criteria, of whom 129 received statins. New-onset diabetes incidence was 16% (n = 49). Statins (p = 0.0004, HR 0.238[0.109-0.524]) and ACE inhibitors/ARB (p = 0.01, HR 0.309[0.127-0.750]) were associated with decreased risk. Prednisone dose (p = 0.0001, HR 1.007[1.003-1.010] per 1 mg/d at 3 months), weight at transplant (p = 0.02, HR 1.022[1.003-1.042] per 1 kg), black ethnicity (p = 0.02, HR 1.230[1.023-1.480]) and age > or =45 years (p = 0.01, HR 2.226[1.162-4.261]) were associated with increased diabetes. Statin use is associated with reduced new-onset diabetes development after renal transplantation.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".