Abstract 14752: FGF-23, Cardiovascular Events, and the Benefit of Canagliflozin in the CANVAS Trial
Bibliographic record
Abstract
Background: The incremental prognostic value of FGF-23 with cardiovascular (CV) risk, composite kidney outcomes (KO) and treatment response was assessed in the CANVAS trial, which studied the efficacy of the sodium glucose co-transporter 2 inhibitor canagliflozin (cana) versus placebo in patients with type 2 diabetes (T2D) and high CV risk. Methods: 3188 patients with available baseline FGF-23 samples were studied. The association between FGF-23 and the composite of time to CV death or hospitalization for heart failure (HHF) (primary endpoint), HHF, CV death, major adverse CV events (MACE) and KO were assessed using multivariable Cox proportional hazard regression models adjusted for clinical risk factors. Events rates by randomized treatment assignment were calculated for FGF-23 after assignment to a ‘low risk’ (Quartiles 1-3) or ‘high risk’ (Q4) group. Results: The median level of FGF-23 was 64.1 RU/mL (IQR 48.3-94.1). A doubling of FGF-23 was significantly associated with the primary endpoint (HR, 1.32; 95% CI, 1.19-1.46; P <0.001) as well as HHF (HR, 1.53; 95% CI, 1.31-1.79, P <0.001), CV death (HR, 1.18; 95% CI, 1.03-1.35, P =0.014), and KO (HR, 1.11; 95% CI, 1.02-1.21, P =0.014) but not MACE (HR, 1.05; 0.95-1.15, P =0.3) in the fully adjusted model (Table 1). Cana yielded consistent risk reduction for the primary endpoint, HHF and KO regardless of whether patients were categorized in the high or low risk group (all P heterogeneity>0.30) (Table 2). Conclusion: High levels of FGF-23 are associated with an increased risk of CV death and HHF, HHF, CV death, and KO in patients with T2D and high CV risk. Treatment with cana provides consistent benefit across higher and lower levels of FGF-23.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".