The hidden lives of medical biomarkers are the focus of a recent study in Nature Communications by Jonathan Mosley, MD, Ph.D., assistant professor of Medicine and Biomedical Informatics, and colleagues from Vanderbilt University Medical Center and 11 other institutions.

Any reliable measure of a physiological state might qualify as a biomarker. Some of the biomarkers used in disease risk assessment and diagnosis are themselves mediators of disease, LDL-cholesterol and blood pressure being two well-known examples.

The study introduces a method to define biomarker-outcomes spectrums. The authors write that, "Defining the complete spectrum of disease outcomes associated with a biomarker not only provides insights into disease mechanisms but also reveals potential beneficial and adverse effects of modulating biomarker levels."

Employing genome-wide trait analysis, the team scans for correlations between 53 medical biomarkers, as measured in an epidemiological study cohort, and 1,139 well-recognized disease outcomes, as reflected in de-identified electronic medical records of a separate genotyped research cohort. In all, the demo involves 44,893 genotyped research subjects.

"Repurposing available genetic data is not only comparatively affordable, but it also provides results immediately." With our method, we are circumventing the decades-long wait for nature to take its course and produce measurable outcomes," the authors noted.

Biomarker-outcome associations

If you measure a group of strangers for both similarities of traits and chance genetic similarity, you can estimate the cumulative genetic influence of genes on each of the measured traits. For each trait, you can also measure correlation with each of the genetic variants you've tested, and calculate a cumulative genetic score for the trait.

Using those scores, they predicted values for the 53 biomarkers in 37,153 genotyped patients seen at VUMC and other centers in the eMERGE Network (Electronic Medical Records and Genomics Network). In this latter group, the team measured associations between the predicted biomarker values and 1,139 diagnoses.

"Along with replicating many known biomarker-outcome associations, we turned up some undescribed associations. However, some seemingly obvious associations were the most surprising to me. For instance, a  biomarker  predicting smoking was associated with diagnoses of tobacco use, alcohol use

"I did not expect that genetics would predict behaviors." This observation has made me a more compassionate physician when approaching patients struggling with these issues, "Mosley said.

The team also found an inverse association between high LDL-cholesterol and septicemia, an association they replicated (without recourse to genotype data) n a separate cohort.