Proteomic Changes in Health Trajectories
Researchers have applied genomic, transcriptomic, and proteomic technologies to assess health trajectories at the molecular level and identify biomarkers of normal, diseased, or aging cells and tissues. Of these, proteins convey the most accurate data about current health status and disease progression that provide some of the most actionable information for healthcare providers. What is lacking is an understanding of how an individual’s proteome is impacted over the course of aging by their lifestyle choices, demographic characteristics and other social determinants of health (SDOH).
One of the first studies that has been proposed using the COLS biobank is a prospective longitudinal study using COLS biobank samples and associated participant data to measure aging trajectories. The concentration of approximately 5,000 proteins in plasma will be measured annually using SomaScan® (SomaLogic, Inc.). Protein profiles previously correlated with aging and threats to aging (e.g. obesity, diabetes, Alzheimer’s disease and related dementias, the top four most prevalent cancers, and heart disease) will be assayed to track health trajectories across five years. These proteomic data will be analyzed alongside health status, lifestyle, demographic and other SDOH data for each participant to identify the impacts of specific and prevalent lifestyle choices to compare aging trajectories and to identify potential biomarkers of protective lifestyle factors against aging.
This proposed research will be the first of its kind to combine extensive and longitudinal proteomic data with detailed health status, lifestyle, demographic, and other SDOH data to investigate the factors that influence aging trajectories. Previous studies have used single-timepoint proteomic profiles from different individuals to identify the typical aging trajectory or longitudinal samples covering less than a year to assess protein profile changes over short time periods. Additionally, no proteomic study of aging has included comprehensive data on factors that might influence the participants’ health trajectories.
The proposed study will address these limitations by tracking protein profiles for participants of a variety of people over time, allowing individual trajectories to be measured and compared to each other and to the typical aging trajectory established in previous proteomic studies. In addition, proteomic assessments of disease using established diagnostic protein profiles will track threats to aging as they develop during the same period. Unlike previous studies, these data will then be compared to the participants’ detailed health status, lifestyle, demographic and other SDOH data to identify factors that influence aging and health trajectories at the proteomic level.
Combining these detailed proteomic and health data on each participant will lead to a more robust and nuanced understanding of the intersections between both the largely predetermined (e.g., sex and race/ethnicity) and chosen (e.g., physical activity and tobacco use) factors that influence health and aging. This approach is anticipated to identify novel mechanisms by which these lifestyle choices, demographic characteristics, and other SDOH impact health over time and inform the development of new therapeutic interventions to foster healthy aging.