Proteinuria or Albuminuria as Markers of Kidney and Cardiovascular Disease Risk
Bibliographic record
Abstract
Background:Urinary albumin–creatinine ratio (UACR) and urinary protein–creatinine ratio (UPCR) are both used in clinical practice to diagnose and monitor chronic kidney disease (CKD). Which measure exhibits stronger associations with clinical outcomes and whether this varies by patient characteristics are unknown. Objective:To assess and compare the performance of UACR and UPCR across CKD-related clinical outcomes. Measurements:We quantified the associations of UACR and UPCR with subsequent clinical outcomes, including kidney failure and cardiovascular events, using Cox proportional hazards regression. Analyses were done in each cohort, followed by random-effects meta-analysis. Subgroups included those based on severity of proteinuria, type 2 diabetes, estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2, and glomerular disease. Results:There were 148 994 participants and 9773 kidney failure events during a median of 3.8 years of follow-up. Higher UACR and UPCR both had a log-linear association with increased risk for kidney failure. The association with kidney failure was somewhat stronger for UACR (adjusted hazard ratio [HR] per SD increment, 2.55 [95% CI, 2.36 to 2.74]) than UPCR (HR, 2.40 [CI, 2.28 to 2.53]; P for comparison<0.001). Results were consistent to slightly stronger in subgroups with UACR greater than 30 mg/g, UPCR greater than 500 mg/g, eGFR less than 60 mL/min/1.73 m2, diabetes, and glomerular disease. Associations between UACR and UPCR were generally similar for cardiovascular outcomes but favored UACR in subgroups with moderately to severely elevated UACR. Conclusion:Overall, UACR was more strongly associated with kidney failure than UPCR (particularly in subgroups with higher UACR), supporting the use of UACR rather than UPCR to diagnose and risk-stratify patients. Primary Funding Source:National Kidney Foundation and National Institute of Diabetes and Digestive and Kidney Diseases.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".