Changes in Albuminuria and the Risk of Major Clinical Outcomes in Diabetes: Results From ADVANCE-ON
Bibliographic record
Abstract
OBJECTIVE: To assess the association between 2-year changes in urine albumin-to-creatinine ratio (UACR) and the risk of clinical outcomes in type 2 diabetes. RESEARCH DESIGN AND METHODS: We analyzed data from 8,766 participants in the Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation Post-Trial Observational Study (ADVANCE-ON). Change in UACR was calculated from UACR measurements 2 years apart, classified into three groups: decrease in UACR of ≥30%, minor change, and increase in UACR of ≥30%. By analyzing changes from baseline UACR groups, categorized into thirds, we repeated these analyses accounting for regression to the mean (RtM). The primary outcome was the composite of major macrovascular events, renal events, and all-cause mortality; secondary outcomes were these components. Cox regression models were used to estimate hazard ratios (HRs). RESULTS: Over a median follow-up of 7.7 years, 2,191 primary outcomes were observed. Increases in UACR over 2 years independently predicted a greater risk of the primary outcome (HR for ≥30% UACR increase vs. minor change: 1.26; 95% CI 1.13-1.41), whereas a decrease in UACR was not significantly associated with lower risk (HR 0.93; 95% CI 0.83-1.04). However, after allowing for RtM, the effect of "real" decrease in UACR on the primary outcome was found to be significant (HR 0.84; 95% CI 0.75-0.94), whereas the estimated effect on an increase was unchanged. CONCLUSIONS: Changes in UACR predicted changes in the risk of major clinical outcomes and mortality in type 2 diabetes, supporting the prognostic utility of monitoring albuminuria change over time.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".