Myocardial performance index improves prediction of major adverse cardiovascular events and heart failure in type 1 and type 2 diabetes without known heart disease: Thousand&1 and Thousand&2 study

Abstract

Abstract Background Cardiovascular disease (CVD) is the leading cause of mortality and morbidity in type 1 (T1D) and type 2 diabetes (T2D). Despite myocardial involvement in diabetes, current risk prediction models do not include parameters of the myocardial function. Myocardial performance index (MPI) reflects left ventricular systolic and diastolic function. The prognostic value of MPI has not been evaluated in larger-scale diabetes populations. Purpose To assess the prognostic value of MPI in a larger-scale diabetes population. Methods We evaluated two prospective cohort studies: The Thousand&1 Study (1093 individuals with T1D) and The Thousand&2 Study (1030 individuals with T2D). Clinical data, including echocardiography, were collected at baseline. We excluded 480 individuals with heart disease. We collected follow-up data from national registries. We defined major adverse cardiovascular events (MACE) as the first incident of all-cause death, hospital admission for acute coronary syndrome, heart failure, or stroke. Results For included individuals (56% male, 54±15 years, MPI 0.51±0.1, 63% T1D), follow-up was 100% after a median of 5.3 years (interquartile range: 4.8 to 6.3). MPI was significantly associated with MACE (Hazard ratio 1.2, 95% confidence interval 1.0-1.3, p=0.012, per 0.10-unit increase) and heart failure (Hazard ratio 1.3, 95% confidence interval 1.1-1.6, p=0.005, per 0.10-unit increase) after adjusting for clinical and echocardiographic variables. MPI predicted MACE better in T1D than in T2D (p=0.031 for interaction). MPI added discriminatory power to the Steno T1D Risk Engine (1,2) in predicting heart failure, but not MACE. Conclusions MPI is an independent predictor of MACE and heart failure in T1D and T2D, with the strongest prediction in T1D. Echocardiographic assessment in diabetes may enhance risk prediction.