Effect of linagliptin versus placebo on cardiovascular and kidney outcomes in nephrotic-range proteinuria and type 2 diabetes: the CARMELINA randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Nephrotic-range proteinuria (NRP) is associated with rapid kidney function loss and increased cardiovascular (CV) disease risk. We assessed the effects of linagliptin (LINA) on CV and kidney outcomes in people with Type 2 diabetes (T2D) with or without NRP. METHODS: Cardiovascular and renal microvascular outcome study with LINA randomized participants with T2D and CV disease and/or kidney disease to LINA 5 mg or placebo (PBO). The primary endpoint [time to first occurrence of 3-point major adverse cardiac events (3P-MACE)], and kidney outcomes, were evaluated by NRP status [urinary albumin:creatinine ratio (UACR) ≥2200 mg/g] at baseline (BL) in participants treated with one or more dose of study medication. RESULTS: ). Over a median of 2.2 years, 3P-MACE occurred with a 2.0-fold higher rate in NRP versus no-NRP (PBO group), with a neutral LINA effect, regardless of NRP. The composite of time to renal death, end-stage kidney disease (ESKD) or decrease of ≥40 or ≥50% in eGFR, occurred with 12.3- and 13.6-fold higher rate with NRP (PBO group); evidence of heterogeneity of effects with LINA was observed for the former [NRP yes/no: hazard ratio 0.80 (0.63-1.01)/1.25 (1.02-1.54); P-interaction 0.005], but not the latter [0.83 (0.64-1.09)/1.17 (0.91-1.51), P-interaction 0.07]. No heterogeneity was observed for renal death or ESKD [0.88 (0.64-1.21)/0.94 (0.67-1.31), P-interaction 0.79]. Glycated haemoglobin A1c (HbA1c) was significantly reduced regardless of NRP, without increasing hypoglycaemia risk. Regression to normoalbuminuria [1.20 (1.07-1.34)] and reduction of UACR ≥50% [1.15 (1.07-1.25)] from BL, occurred more frequently with LINA, regardless of NRP status (P-interactions >0.05). CONCLUSIONS: Individuals with T2D and NRP have a high disease burden. LINA reduces their albuminuria burden and HbA1c, without affecting CV or kidney risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".