Change in urine albumin-to-creatinine ratio and clinical outcomes in patients with chronic kidney disease and type 2 diabetes
Bibliographic record
Abstract
INTRODUCTION: This study aims to investigate the association between change in urine albumin-to-creatinine ratio (UACR) and clinical outcomes in patients with chronic kidney disease (CKD) and type 2 diabetes. RESEARCH DESIGN AND METHODS: Adult patients with elevated UACR (≥30 mg/g in initial testing) after the diagnosis of type 2 diabetes and CKD were identified from the Optum electronic health records database (01/2007-09/2021). UACR change from initial to last test (6-24 months) was categorized as >30% decrease, stable (-30% to 30%), or >30% increase. Risk of all-cause mortality, composite cardiovascular (CV) outcome (CV death, myocardial infarction, stroke, and hospitalization for heart failure), and CKD progression (≥40% decline in estimated glomerular filtration rate or kidney failure) were estimated with Cox proportional hazard models adjusted for baseline characteristics. RESULTS: Compared with patients with a stable UACR (n=35 117), those with a >30% UACR decrease (n=89 562) had lower risk of all-cause mortality (adjusted HR (aHR)=0.93, 95% CI 0.90 to 0.96), composite CV outcomes (aHR=0.93, 95% CI 0.90 to 0.95), and CKD progression (aHR=0.84, 95% CI 0.81 to 0.86) (all p<0.001), and patients with a >30% UACR increase (n=35 703) had higher risk of each endpoint (aHR=1.24, 95% CI 1.19 to 1.28; aHR=1.24, 95% CI 1.20 to 1.28; and aHR=1.41, 95% CI 1.36 to 1.46, respectively; all p<0.001). CONCLUSIONS: In patients with CKD and type 2 diabetes, a >30% UACR decrease was associated with lower risk of mortality, CV events, and CKD progression, whereas a >30% UACR increase was associated with higher risk of these clinical outcomes. These findings highlight the importance of albuminuria monitoring and potential clinical benefits of targeted UACR reductions in this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".