Cardiovascular and renal outcomes with varying degrees of kidney disease in high‐risk people with type 2 diabetes: An epidemiological analysis of data from the AMPLITUDE‐O trial
Bibliographic record
Abstract
AIMS: To estimate the incidence of a major adverse cardiovascular event (MACE) and a composite kidney outcome across estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) levels, and to determine whether efpeglenatide's effect varies with these indices. MATERIALS AND METHODS: AMPLITUDE-O trial data were used to estimate the relationship of eGFR, UACR, and Kidney Disease Improving Global Outcomes (KDIGO) category to the hazard of MACE and the kidney composite. Interactions on these outcomes between eGFR and the UACR, and between each of these variables and efpeglenatide were also assessed. RESULTS: Baseline eGFR and UACR were available for 3983 participants (mean age 64.5 years). During a median follow-up of 1.8 years, the hazards of MACE and the kidney composite for the lowest versus highest eGFR third were 1.6 (95% confidence interval [CI] 1.2, 2.2) and 2.3 (95% CI 1.9, 2.8), respectively. The hazards for the highest versus the lowest UACR third were 2.3 (95% CI 1.8, 3.1) and 18.0 (95% CI 12.7, 25.5), respectively, and for the high- versus low-risk KDIGO categories the hazards were 2.4 (95% CI 1.8, 3.1) and 16.0 (95% CI 11.6, 22.0), respectively. eGFR and UACR were independent determinants of both outcomes, but negatively interacted with each other for the kidney outcome. Efpeglenatide's effect on both outcomes did not vary with any kidney disease measure (all interaction p values ≥0.26). CONCLUSIONS: In high-risk people with diabetes, eGFR, UACR, and KDIGO category have different relationships to incident cardiovascular and kidney outcomes. The beneficial effect of efpeglenatide on these outcomes is independent of kidney-related risk category.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".