Abstract
Just one biomarker won’t tell you whether you will enjoy a healthy dotage or suffer a debilitating age-related disease. Two or three biomarkers might not suffice, either. But how about 19? That’s how many biomarkers figured into a panel developed by scientists based at Boston University Medical Center.
Although the 19 biomarkers have their ups and downs, they settle into 26 different signatures of aging—some of which auger well, and some, otherwise. These biomarkers are grist not for divination, but a systems analysis approach that simultaneously integrates multiple biomarkers that vary with age, including biomarkers specific for inflammatory, hematological, metabolic, and hormonal functions.
The approach was detailed in an article (“Biomarker Signatures of Aging”) that appeared in Aging Cell. “The intuition of the approach,” the article’s authors wrote, “is that in a sample of individuals of different ages, there will be an ‘average distribution’ of circulating biomarkers that represents a prototypical signature of average aging. Additional signatures that may correlate to varying aging patterns, for example, disease-free aging, or aging with increased risk for diabetes or cardiovascular disease, will be characterized by a departure of subsets of the circulating biomarkers from the average distribution.”
Using blood biomarker data from almost 5,000 participants in the Long Life Family Study, the Boston University researchers found that many people—about half—have an average pattern. But smaller groups of people have specific patterns that deviate from the norm and are associated with increased probabilities that particular medical conditions, lower levels of physical function, or higher mortality will become apparent eight years hence.
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“Many prediction and risk scores already exist for specific diseases,” says Paola Sebastiani, Ph.D., a study co-leader. “Here, though, we are showing that particular patterns of groups of biomarkers can indicate how well a person is aging.” Another study co-leader, Thomas Perls, M.D., adds the study shows how proteomics and metabolomics, through Big Data, may constitute the next revolution in predictive medicine and drug discovery. It may no longer be necessary for researchers of aging to “wait years and years for clinical outcomes to occur,” notes Dr. Sebastiani.