Modern epidemiology suggests a potential interactive association between diet, lifestyle, genetics and the risk of many chronic diseases. As such, many epidemiologic studies attempt to consider assessment of dietary intake alongside genetic measures and other variables of interest. However, given the multi-factorial complexities of dietary exposures, all dietary intake assessment methods are associated with measurement errors which affect dietary estimates and may obscure disease risk associations. For this reason, dietary biomarkers measured in biological specimens are being increasingly used as additional or substitute estimates of dietary intake and nutrient status. Genetic variation may influence dietary intake and nutrient metabolism and may affect the utility of a dietary biomarker to properly reflect dietary exposures. Although there are many functional dietary biomarkers that, if utilized appropriately, can be very informative, a better understanding of the interactions between diet and genes as potentially determining factors in the validity, application and interpretation of dietary biomarkers is necessary. It is the aim of this review to highlight how some important biomarkers are being applied in nutrition epidemiology and to address some associated questions and limitations. This review also emphasizes the need to identify new dietary biomarkers and highlights the emerging field of nutritional metabonomics as an analytical method to assess metabolic profiles as measures of dietary exposures and indicators of dietary patterns, dietary changes or effectiveness of dietary interventions. The review will also touch upon new statistical methodologies for the combination of dietary questionnaire and biomarker data for disease risk assessment. It is clear that dietary biomarkers require much further research in order to be better applied and interpreted. Future priorities should be to integrate high quality dietary intake information, measurements of dietary biomarkers, metabolic profiles of specific dietary patterns, genetics and novel statistical methodology in order to provide important new insights into gene-diet-lifestyle-disease risk associations.