Paolo Vineis1, Marc Chadeau-Hyam. 1. MRC/HPA Centre for Environment and Health, School of Public Health, Imperial College London, London, UK. p.vineis@imperial.ac.uk
Abstract
PURPOSE OF REVIEW: Biomarkers play a central role in chronic disease epidemiology, providing insights into pathways related to the relationship between environmental exposures and disease risk. Recent developments in both data acquisition techniques and laboratory approaches advocate for a more extensive and refined use of biomarkers. RECENT FINDINGS: We review some issues related to biomarker identification and validation techniques as well as the main methodologies to measure biomarkers in existing biobank data. Finally, we describe analytical strategies recently proposed to include the time component into biomarker research. SUMMARY: This review suggests that some of the technical issues to identify, validate, and analyze biomarkers have been partly addressed in epidemiological studies. The inclusion of biomarker analyses into longitudinal frameworks provides a promising potential to analyze the role of different types of biomarkers and to refine the 'causal' models linking exposure to disease risk. These kinds of approaches can be implemented based on existing cohort data, at the cost of some approximation, but their generalization would ideally require advancements in study design, such as routinely allowing for the collection of several biological samples at different time points.
PURPOSE OF REVIEW: Biomarkers play a central role in chronic disease epidemiology, providing insights into pathways related to the relationship between environmental exposures and disease risk. Recent developments in both data acquisition techniques and laboratory approaches advocate for a more extensive and refined use of biomarkers. RECENT FINDINGS: We review some issues related to biomarker identification and validation techniques as well as the main methodologies to measure biomarkers in existing biobank data. Finally, we describe analytical strategies recently proposed to include the time component into biomarker research. SUMMARY: This review suggests that some of the technical issues to identify, validate, and analyze biomarkers have been partly addressed in epidemiological studies. The inclusion of biomarker analyses into longitudinal frameworks provides a promising potential to analyze the role of different types of biomarkers and to refine the 'causal' models linking exposure to disease risk. These kinds of approaches can be implemented based on existing cohort data, at the cost of some approximation, but their generalization would ideally require advancements in study design, such as routinely allowing for the collection of several biological samples at different time points.