Albuminuria and Kidney Function Independently Predict Cardiovascular and Renal Outcomes in Diabetes
Bibliographic record
Abstract
There are limited data regarding whether albuminuria and reduced estimated GFR (eGFR) are separate and independent risk factors for cardiovascular and renal events among individuals with type 2 diabetes. The Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation (ADVANCE) study examined the effects of routine BP lowering on adverse outcomes in type 2 diabetes. We investigated the effects of urinary albumin-to-creatinine ratio (UACR) and eGFR on the risk for cardiovascular and renal events in 10,640 patients with available data. During an average 4.3-yr follow-up, 938 (8.8%) patients experienced a cardiovascular event and 107 (1.0%) experienced a renal event. The multivariable-adjusted hazard ratio for cardiovascular events was 2.48 (95% confidence interval 1.74 to 3.52) for every 10-fold increase in baseline UACR and 2.20 (95% confidence interval 1.09 to 4.43) for every halving of baseline eGFR, after adjustment for regression dilution. There was no evidence of interaction between the effects of higher UACR and lower eGFR. Patients with both UACR >300 mg/g and eGFR <60 ml/min per 1.73 m(2) at baseline had a 3.2-fold higher risk for cardiovascular events and a 22.2-fold higher risk for renal events, compared with patients with neither of these risk factors. In conclusion, high albuminuria and low eGFR are independent risk factors for cardiovascular and renal events among patients with type 2 diabetes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".