Statisticians Seek Improved Biomarker Utility
When you hear “biomarker,” what is the first thing that comes to mind? BRCA1? Fluorescently labeled G-proteins? Those are two well-known examples, but biomarkers (short for “biological markers”) are actually a much broader group of biological signs than just genetic or cellular traits.
Defined by the NIH Biomarkers Definitions Working Group in 1998, a biomarker is categorized as “a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” This means that they can range broadly from blood pressure readings, to genetic risk factors, to the presence (or absence) of infectious agents.
As a natural result of their ubiquity, biomarkers are regarded as a clinically valid, reproducible way of quantifying the significance of biological signs. In turn, clinicians and researchers around the world have begun using patients’ individual biomarkers as a powerful method of predicting and treating a plethora of health issues from cancer, to infertility, to genetic diseases.
However, the excitement over using biomarkers as a predictive tool comes with some fine print: due to the limited breadth of biomarkers, they should be assessed in conjunction with one another over time to provide the most effective diagnosis possible.
IRP researchers are tackling these problems in the field. Biostatistics researcher Aiyi Liu, Ph.D., is developing more efficient ways of determining the true effectiveness of biomarkers in disease prediction and diagnosis. A member of the Biostatistics and Bioinformatics Branch of the National Institute of Child Health and Human Development (NICHD) since 2002, Aiyi’s research investigates the validity of biomarkers as diagnostic tools. By using advanced statistical methods, his research provides a scale of effectiveness that can be used theoretically to compare which biomarkers would be best given a patient’s particular disease area.
Danping Liu, Ph.D., also from the Biostatistics and Bioinformatics Branch of NICHD, studies how combinations of biomarkers can further personalize disease risk prediction and diagnoses. His research at the IRP suggests that in order for clinicians to fully utilize the power of biomarkers as a diagnostic tool, doctors must collect and analyze as much information about their patients as possible. Therefore, demographic information, body temperature, known allergies and other personal facts should all be considered in conjugation with multiple traditional biomarkers in order to provide a personalized level of medicinal care.
What’s exciting about this research is that we’re moving from using biomarkers as tools of observation to being able to use them as tools of prediction. Although we have a long way to go before a virtual catalogue of biomarkers is available for cross-reference in disease detection and prevention, our researchers at the IRP are working towards the development of just that—a tool that will be essential for clinicians continuing to make advances in this exciting new era of personalized medicine.
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This page was last updated on Monday, January 29, 2024