Cardiovascular Effects of Canagliflozin in Relation to Renal Function and Albuminuria
Bibliographic record
Abstract
BACKGROUND: People with type 2 diabetes mellitus (T2DM) have elevated cardiovascular (CV) risk, including for hospitalization for heart failure (HHF). Canagliflozin reduced CV and kidney events in patients with T2DM and high CV risk or nephropathy in the CANVAS (CANagliflozin cardioVascular Assessment Study) Program and the CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation) trial. OBJECTIVES: The aim of this study was to assess the effects of canagliflozin on CV outcomes according to baseline estimated glomerular filtration rate (eGFR) and urine albumin:creatinine ratio (UACR) in pooled patient-level data from the CANVAS Program and CREDENCE trial. METHODS: ) and UACR (<30, 30-300, and >300 mg/g). HRs and 95% CIs were estimated by using Cox regression models overall and according to subgroups. RESULTS: , and 31.9% with UACR >300 mg/g. Rates of CV death or HHF increased as eGFR declined and/or UACR increased. Canagliflozin significantly reduced CV death or HHF compared with placebo (19.4 vs 28.0 events per 1,000 patient-years; HR: 0.70; 95% CI: 0.62-0.79), with consistent results across eGFR and UACR categories (all P interaction >0.40). CONCLUSIONS: Risk of CV death or HHF was higher in those with lower baseline eGFR and/or higher UACR. Canagliflozin consistently reduced CV death or HHF in participants with T2DM and high CV risk or nephropathy regardless of baseline renal function or level of albuminuria. (Canagliflozin Cardiovascular Assessment Study [CANVAS], NCT01032629; A Study of the Effects of Canagliflozin [JNJ-24831754] on Renal Endpoints in Adult Participants With Type 2 Diabetes Mellitus [CANVAS-R], NCT01989754; and Evaluation of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Participants With Diabetic Nephropathy [CREDENCE], NCT02065791).
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".