Statistics

Normality was assessed for continuous variables using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation. Non-normally distributed data are presented as median [interquartile range]. Comparisons were performed for continuous variables using the parametric Student t-test or the non-parametric Mann-Whitney U test. Categorical variables are presented as number (percentage) and compared using the χ² test.

Univariable Cox regression analysis was performed to identify prognostic variables in the entire cohort and also in the diabetes subgroup. Clinically relevant variables (age, sex, body mass index, diabetes, smoking, coronary artery disease, hypertension, New York Heart Association functional class, and systolic blood pressure), circulating biomarkers (NT-proBNP, hs-TnT, GDF-15, sST2, and Gal-3) and CMR markers of function, remodelling and fibrosis (LV end diastolic volume index, mass index, ejection fraction, GLS, LGE, Native T1, and ECV) were tested in the Cox models. Covariates with a p-value < 0.05 in univariable analyses were entered into the multivariable Cox model to identify independently prognostic variables, using forward stepwise selection (probability to remove threshold ≥0.1). Event-free survival curves were examined using the Kaplan-Meier method and compared with the log-rank test. In the Kaplan-Meier analysis, GLS was treated as a binary variable according to the cohort median, in order to maximise statistical power. Statistical analyses were performed using SPSS Version 28 (Statistical Package for the Social Sciences, International Business Machines, Inc., Armonk, New York, USA) and GraphPad Prism 9.4.1 (GraphPad Software, Inc., San Diego, California, USA). A two-sided p-value < 0.05 was considered as statistically significant.