Risk factors for kidney disorders in patients with type 2 diabetes at high cardiovascular risk: An exploratory analysis (DEVOTE 12)
Bibliographic record
Abstract
Aim: To investigate risk factors associated with kidney disorders in patients with type 2 diabetes (T2D) at high cardiovascular (CV) risk. Methods: In DEVOTE, a cardiovascular outcomes trial, 7637 patients were randomised to insulin degludec (degludec) or insulin glargine 100 units/mL (glargine U100), with standard of care. In these exploratory post hoc analyses, serious adverse event reports were searched using Standardised MedDRA ® Queries related to chronic kidney disease (CKD) or acute kidney injury (AKI). Baseline predictors of CKD, AKI and change in estimated glomerular filtration rate (eGFR) were identified using stepwise selection and Cox or linear regression. Results: Over 2 years, eGFR (mL/min/1.73 m 2 ) decline was small and similar between treatments (degludec: 2.70; glargine U100: 2.92). Overall, 97 and 208 patients experienced CKD and AKI events, respectively. A history of heart failure was a risk factor for CKD (hazard ratio [HR] 1.97 [95% confidence interval [CI] 1.41; 2.75]) and AKI (HR 2.28 [95% CI 1.64; 3.17]). A history of hepatic impairment was a significant predictor of CKD (HR 3.28 [95% CI 2.12; 5.07]) and change in eGFR (estimate: −8.59 [95% CI −10.20; −7.00]). Conclusion: Our findings indicate that traditional, non-modifiable risk factors for kidney disorders apply to insulin-treated patients with T2D at high CV risk. Trial registration: NCT01959529 (ClinicalTrials.gov).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".