FGF-23, hsCRP, Cardiovascular Events, and the Benefit of Canagliflozin in the CANVAS Trial
Bibliographic record
Abstract
BACKGROUND: The CANVAS (Canagliflozin Cardiovascular Assessment Study) trial provided the opportunity to determine the utility of measuring cardiorenal biomarkers, such as fibroblast growth factor 23 (FGF-23) and high-sensitivity C-reactive protein (hsCRP) levels for determining risk prediction and treatment response to sodium glucose co-transporter 2 inhibitor therapy in patients with type 2 diabetes mellitus. OBJECTIVES: The prognostic value of these biomarkers for predicting adverse cardiovascular (CV) outcomes and treatment response was assessed. METHODS: Of 4,330, 3,188 (73.6%) participants had available longitudinal biomarker samples. The association between FGF-23 and hsCRP with composite CV death or hospitalization for heart failure (HHF), HHF, CV death, and major adverse CV events were assessed using multivariable Cox proportional hazard models adjusted for clinical risk factors, and markers of cardiac and renal injury. Event rates by randomized treatment assignment were calculated for FGF-23 and hsCRP after assignment to a "low-risk" (quartiles [Q] 1-3) or "high-risk" (Q4) group. Multimarker risk assessment was done by stratifying participants by both FGF-23 and hsCRP quartiles to create 4 risk groups. RESULTS: When compared with Q1, FGF-23 levels in Q4 were significantly associated with CV death/HHF (HR: 1.65; 95% CI: 1.15-2.40; P = 0.008) and HHF (HR: 1.96; 95% CI: 1.04-3.69; P = 0.037) whereas hsCRP levels in Q4 were significantly associated with CV death (HR: 1.78; 95% CI: 1.16-2.73; P = 0.008) and major adverse CV events (HR: 1.35; 95% CI: 1.02-1.78; P = 0.038) in adjusted analyses. There was consistent effect of canagliflozin vs placebo across high- and low-risk groups (P-interactions ≥0.30). CONCLUSIONS: FGF-23 and hsCRP are biomarkers associated with increased CV risk, but these markers did not identify participants who preferentially benefited from treatment with canagliflozin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".