Biomarkers for the prediction of type 2 diabetes and cardiovascular disease

Abstract

Risk prediction for type 2 diabetes (T2D) and cardiovascular disease (CVD) remains suboptimal even after the introduction of global risk assessment by various scores. This has prompted the search for additional biomarkers. A variety of blood biomarkers representing various pathophysiological pathways of insulin resistance and atherosclerosis, as well as markers of subclinical disease and genetic markers, have been investigated. This review provides an overview of studies assessing the clinical utility of various biomarkers on the basis of hypothesis-driven selection as well as hypothesis-free approaches from novel "-omics" technologies. So far, the assessment of genotypes and of several candidate biomarkers from blood has resulted in only small improvements in the accuracy of prediction of CVD and T2D over and above that predicted on the basis of established risk factors. Integrated approaches, combining biomarkers from genomics, transcriptomics, proteomics, and metabolomics, as well as serial measurements of biomarkers, are required to make a complete assessment of the potential clinical usefulness of biomarkers for risk prediction of cardiometabolic disease.