Changes in Albuminuria Predict Cardiovascular and Renal Outcomes in Type 2 Diabetes: A Post Hoc Analysis of the LEADER Trial
Bibliographic record
Abstract
OBJECTIVE A post hoc analysis to investigate the association between 1-year changes in albuminuria and subsequent risk of cardiovascular and renal events. RESEARCH DESIGN AND METHODS LEADER was a randomized trial of liraglutide up to 1.8 mg/day versus placebo added to standard care for 3.5–5 years in 9,340 participants with type 2 diabetes and high cardiovascular risk. We calculated change in urinary albumin-to-creatinine ratio (UACR) from baseline to 1 year in participants with >30% reduction (n = 2,928), 30–0% reduction (n = 1,218), or any increase in UACR (n = 4,124), irrespective of treatment. Using Cox regression, risks of major adverse cardiovascular events (MACE) and a composite nephropathy outcome (from 1 year to end of trial in subgroups by baseline UACR [<30 mg/g, 30–300 mg/g, or ≥300 mg/g]) were assessed. The analysis was adjusted for treatment allocation alone as a fixed factor and for baseline variables associated with cardiovascular and renal outcomes. RESULTS For MACE, hazard ratios (HRs) for those with >30% and 30–0% UACR reduction were 0.82 (95% CI 0.71, 0.94; P = 0.006) and 0.99 (0.82, 1.19; P = 0.912), respectively, compared with any increase in UACR (reference). For the composite nephropathy outcome, respective HRs were 0.67 (0.49, 0.93; P = 0.02) and 0.97 (0.66, 1.43; P = 0.881). Results were independent of baseline UACR and consistent in both treatment groups. After adjustment, HRs were significant and consistent in >30% reduction subgroups with baseline micro- or macroalbuminuria. CONCLUSIONS A reduction in albuminuria during the 1st year was associated with fewer cardiovascular and renal outcomes, independent of treatment. Albuminuria monitoring remains an important part of diabetes care, with great unused potential.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".