The use of biomarkers in cardiovascular medicine is fairly well established for the diagnosis of acute ischemic syndromes and heart failure using high-sensitivity troponin (hsTn) tests or natriuretic peptides (NT-proBNP, BNP). Related to other potential applications like risk stratification, monitoring of disease and selection of therapy, biomarkers are less well established. The pathophysiology of atherosclerosis is a complex process with many underlying pathways. Out of the many candidates that have been investigated during recent years, markers from mainly 3 areas have found to be useful for risk prediction, which are troponins, natriuretic peptides, and markers of inflammation, in particular C-reactive protein. These might be used in addition to scores but could also be used as biomarkers on their own. The problem at present is that none of the cardiovascular societies endorses their use for risk stratification in primary prevention and only accepts an imaging biomarker, coronary artery calcium (CAC) measurements or plaque determination by carotid ultrasound in those at intermediate risk. In secondary prevention it has been shown, based on the STABILITY study for example, that NT-proBNP outperforms all other biomarkers although troponins were next. Serial measurements are of increasing interest and some studies have related changes in troponins clearly to worse outcome. In addition, troponins might also be used as a response marker in patients receiving statins since WOSCOPS has clearly shown that those with the strongest decrease in troponin after the initiation of pravastatin therapy had the greatest benefit. Furthermore, both, natriuretic peptides and troponins might be used to further identify high risk subgroups among hypertensives and might lead to a more intensified therapy. Research at present focuses on high throughput extensive measurements of large numbers of biomarkers due to the availability of chip-based immunoassays or Aptamer-based assays which enables the measurement of several hundreds or proteins. These novel technologies for the measurement of biomarkers might be complemented by the introduction of artificial intelligence to better understand their interaction and improve the performance of our efforts to better risk stratify patients in the context of personalized medicine.