A set of biomarker signatures that suggest how well a person is aging and the risk for aging-related diseases has been identified by researchers at Boston University.
The finding opens the door to a molecular-based definition of aging that uses information from multiple biomarkers to create signatures associated with different mortality and morbidity risks. Further research is needed to better characterize the signatures.
“These signatures depict differences in how people age, and they show promise in predicting healthy aging, changes in cognitive and physical function, survival, and age-related diseases like heart disease, stroke, type 2 diabetes, and cancer,”
the researchers say.
26 Biomarker Signatures
The study used biomarker data collected from the blood samples of almost 5,000 participants in the Long Life Family Study.
The researchers found that a large number of people — about half — had an average “signature,” or pattern, of 19 biomarkers. Smaller groups of people had specific patterns of those biomarkers that deviated from the norm and were associated with increased probabilities of association with particular medical conditions, levels of physical function, and mortality risk eight years later.
For example, one pattern was associated with disease-free aging, another with dementia, and another with disability-free aging in the presence of cardiovascular disease.
In all, the researchers generated 26 different predictive biomarker signatures.
Boston University’s Paola Sebastiani, a professor of biostatistics at the School of Public Health, and Thomas Perls, a professor at the School of Medicine and one of the principal investigators of the Long Life Family Study, led the study.
“Many prediction and risk scores already exist for predicting specific diseases like heart disease,” Sebastiani says. “Here, though, we are taking another step by showing that particular patterns of groups of biomarkers can indicate how well a person is aging and his or her risk for specific age-related syndromes and diseases.”
Big Data, Proteomics And Metabolomics
Perls says the study is an example of the usefulness of “big data” and the emerging research fields of proteomics and metabolomics.
“We can now detect and measure thousands of biomarkers from a small amount of blood, with the idea of eventually being able to predict who is at risk of a wide range of diseases—long before any clinical signs become apparent,” says Perls.
Sebastiani says that the analytic methods used in the research make studies of drug and other medical interventions to prevent or delay age-related diseases much more plausible, since clinical trials “may not have to wait years and years for clinical outcomes to occur.” Instead, trials may be able to rely on biomarker signatures much earlier, “to detect the effects, or absence of effects, that they are searching for,” she says.
She and Perls say researchers are just beginning to break ground on the usefulness of biomarker signatures.
“Following all the recent advances in genetics, the science of proteomics and metabolomics is the next big revolution in predictive medicine and drug discovery,” says Perls.
The National Institutes of Health and the Samowitz Family Foundation funded the study.
Sebastiani, P., Thyagarajan, B., Sun, F., Schupf, N., Newman, A. B., Montano, M. and Perls, T. T. (2017) Biomarker signatures of aging Aging Cell. doi:10.1111/acel.12557
Image: David Gregory & Debbie Marshall, Wellcome Images