The relationship between eGFR slope and subsequent risk of vascular outcomes and all-cause mortality in type 2 diabetes: the ADVANCE-ON study
Bibliographic record
Abstract
AIMS/HYPOTHESIS: Some studies have reported that annual change in eGFR (eGFR slope) is associated with the future risk of end-stage kidney disease, cardiovascular disease and death in general or chronic kidney disease cohorts. However, the benefits of using eGFR slopes for prediction of major clinical outcomes in diabetes are unclear. METHODS: We used data from the Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation (ADVANCE) trial and the ADVANCE Post-Trial Observational Study (ADVANCE-ON). After excluding the first 4 months during which an acute fall in eGFR was induced by the initiation of an ACE inhibitor and diuretic combination agent, eGFR slopes were estimated by linear mixed models, using three measurements of eGFR at 4, 12 and 24 months after randomisation over 20 months, and categorised according to quartiles. Cox regression models were used to evaluate adjusted HRs for the study's primary outcome, a composite of major renal events, major macrovascular events and all-cause mortality during the subsequent follow-up from 24 months after randomisation. RESULTS: ) was significantly associated with the subsequent risk of the primary outcome (HR 1.30 [95% CI 1.17, 1.43]) compared with a stable change in eGFR (middle 50%, -1.63 to 0.33). An annual substantial increase in eGFR (highest 25%, >0.33) had no significant association with the risk of the primary outcome (HR 0.96 [95% CI 0.86, 1.07]). CONCLUSIONS/INTERPRETATION: Our study supports the utility of eGFR slope in type 2 diabetes as a surrogate endpoint for renal outcomes, as well as a prognostic factor for identifying individuals at high risk of cardiovascular disease and all-cause mortality. TRIAL REGISTRY NUMBER: ClinicalTrials.gov registration no. NCT00145925 and no. NCT00949286.
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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 | 0.000 |
| 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 | 0.000 |
| 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".