Abstract
Background: Newer therapeutic agents for type 2 diabetes mellitus can improve cardiovascular outcomes, but diabetes remains underdiagnosed in patients with myocardial infarction (MI). We sought to identify proteomic markers of undetected dysglycaemia (impaired fasting glucose, impaired glucose tolerance, or diabetes mellitus) to improve the identification of patients at highest risk for diabetes. Materials and methods: In this prospective cohort, 626 patients without known diabetes underwent oral glucose tolerance testing (OGTT) during admission for MI. Proximity extension assay was used to measure 81 biomarkers. Multivariable logistic regression, adjusting for risk factors, was used to evaluate the association of biomarkers with dysglycaemia. Subsequently, lasso regression was performed in a 2/3 training set to identify proteomic biomarkers with prognostic value for dysglycaemia, when added to risk factors, fasting plasma glucose, and glycated haemoglobin A1c. Determination of discriminatory ability was performed in a 1/3 test set. Results: In total, 401/626 patients (64.1%) met the criteria for dysglycaemia. Using multivariable logistic regression, cathepsin D had the strongest association with dysglycaemia. Lasso regression selected seven markers, including cathepsin D, that improved prediction of dysglycaemia (area under the receiver operator curve [AUC] 0.848 increased to 0.863). In patients with normal fasting plasma glucose, only cathepsin D was selected (AUC 0.699 increased to 0.704). Conclusions: Newly detected dysglycaemia, including manifest diabetes, is common in patients with acute MI. Cathepsin D improved the prediction of dysglycaemia, which may be helpful in the a priori risk determination of diabetes as a motivation for confirmatory OGTT.