A novel kidney disease index reflecting both the albumin-to-creatinine ratio and estimated glomerular filtration rate, predicted cardiovascular and kidney outcomes in type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: The estimated glomerular filtration rate (eGFR) and the albumin-to-creatinine ratio (ACR) are risk factors for diabetes-related outcomes. A composite that captures information from both may provide a simpler way of assessing risk. METHODS: 9115 of 9901 Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) participants with both an ACR and eGFR at baseline were included in this post hoc epidemiologic analysis. The hazard of higher baseline levels of 1/eGFR and natural log transformed ACR (calculated as ln [ACR × 100] to eliminate negative values) and their interaction for incident major adverse cardiovascular events (MACE), kidney outcomes, and deaths was estimated. The hazard of the geometric mean of these two baseline measures (the kidney disease index or KDI) was also assessed. RESULTS: A non-linear relationship was observed between 1/eGFR and all three outcomes, and between ln [ACR × 100] and the kidney outcome. There was also a negative interaction between these two risk factors with respect to MACE and death. Conversely, a linear relationship was noted between the KDI and all three outcomes. People in the highest KDI fifth experienced the highest incidence of MACE, death, and the kidney outcome (4.43, 4.56, and 5.55/100 person-years respectively). C statistics for the KDI were similar to those for eGFR and albuminuria. CONCLUSIONS: The KDI combines the baseline eGFR and ACR into a novel composite risk factor that has a simple linear relationship with incident serious outcomes in people with diabetes and additional CV risk factors. Trial Registration clinicaltrials.gov NCT01394952.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".