Proteinuria and Rate of Change in Kidney Function in a Community-Based Population
Bibliographic record
Abstract
Proteinuria identifies patients at risk for adverse clinical outcomes, but it is unclear whether proteinuria correlates with the rate of renal decline. We examined the association between proteinuria and rate of change in estimated GFR (eGFR) in a cohort of 638,150 adults from a province-wide registry in Alberta, Canada, who had a measure of proteinuria and three or more outpatient serum creatinine measurements over a period of ≥1 year. An adjusted sex-specific linear mixed-effects model was used to determine the rate of change in eGFR per year for patients with normal, mild, and heavy proteinuria, stratified by baseline kidney function (eGFR ≥90, 60-89.9, 45-59.9, 30-44.9, and 15-29.9 ml/min per 1.73 m(2)). In men, heavy proteinuria and a baseline eGFR of 45-59.9 ml/min per 1.73 m(2) correlated with a change in eGFR of -2.16 (95% confidence interval [CI], -2.37 to -1.95) ml/min per 1.73 m(2) per year, whereas mild proteinuria and a baseline eGFR of 30-44.9 ml/min per 1.73 m(2) correlated with a change in eGFR of -0.51 (95% CI, -0.70 to -0.32) ml/min per 1.73 m(2) per year. Similar trends were observed for female, elderly, and diabetic patients. Notably, normal protein levels and a lower baseline eGFR (15-29.9 ml/min per 1.73 m(2)) correlated with stable or improved renal function. In conclusion, our results suggest that proteinuria of increasing severity is associated with a faster rate of renal decline, regardless of baseline eGFR, and the combined effect should be considered in patients with CKD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".