Statistical analysis
REGRESSION, DIABETES MELLITUS
Biomarkers were log transformed because of their skewed distribution. Median (interquartile) and count (frequency) were used to present continuous and categorical variables. Kruskal–Wallis, ANOVA, and chi-square tests were used to compare the baseline characteristics of study population across IGF-1 quartiles, IGFBP-3, and IGF-1/IGFBP-3 ratio as appropriate. To evaluate the effect of canagliflozin on biomarker concentrations, comparisons of geometric mean (95% CI) concentrations were performed in Years 1 and 3. For change from baseline to Year 1, a base linear model was constructed for each log-transformed biomarker at Year 1 by selecting important baseline covariates in patients randomized to placebo in the main study based on Bayesian Information Criterion. The candidate covariates at baseline included continuous variables: age, eGFR, body mass index, systolic blood pressure, hemoglobin A1c, duration of diabetes mellitus, UACR, log transformed NTproBNP and categorical variables: history of HF, and history of diuretic treatment. Cox proportional hazard regression was implemented to assess the association between biomarker concentrations with clinical outcomes, including treatment and treatment-by-biomarker interaction in the models with selected covariates. To do so, log-transformed concentrations of IGF-1 and IGFBP-3 or their ratio were evaluated with hazard ratio (HR) and 95% CI expressed per 1-unit change in each measure. Additionally, dichotomous cutoffs for IGF-1 based on age were also applied All hypotheses were 2-sided, with a p-value < 0.05 considered statistically significant. All statistical analyses were performed using the R version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria. URL: 