Identifying Proteomic Profiles Associated with Aging
Many research groups are using the extensive data that can be gathered on protein levels, transcription, or epigenetic marks in order to construct clocks that measure the impact of aging on an individual. All of this data changes from moment to moment and from year to year, alongside health, environment, and the biological damage of aging. Accuracy in terms of correlation with chronological or biological age varies widely, but at a few of the clocks are quite good in this regard. The work here is one example of many similar projects presently underway, in which data is sifted in search of protein levels that change in characteristic ways with age.
The present study identified proteomic profiles associated with chronological age and proteomic signatures related to aging phenotypes in a unique population of older adults. Maintenance of homeostasis is important in successful aging, whereas major deviations from stable physiology that can be captured by changes in the proteome may reflect accelerated aging and disease prevalence. Our findings demonstrated that individuals with a family history of longevity exhibit a proteome that is suggestive of delayed aging. Additionally, we showed that clusters of proteins, which were associated with age, were also related to complex diseases and other age-associated phenotypes.
We hypothesized that the proteome can capture the biology underlying the physiological age and not simply the chronological age. We tested this hypothesis in a homogenous community-dwelling cohort of Ashkenazi Jewish older adults in whom ~4,265 plasma proteins were measured. As part of the study, we aimed to develop an age prediction model based on the proteome and to test whether it predicted mortality. In addition, our cohort was enriched with individuals with familial longevity, with approximately half of the cohort composed of offspring of parents with exceptional longevity who repeatedly demonstrated better health status compared to age-matched controls
In the 1,025 participants of the LonGenity cohort (age range: 65-95, 55.7% females), we found that 754 of 4,265 proteins were associated with chronological age. Pleiotrophin (PTN), WNT1-inducible-signaling pathway protein 2 (WISP-2), chordin-like protein 1 (CRDL1), transgelin (TAGL), and R-spondin-1(RSPO1), were the proteins most significantly associated with age. Weighted gene co-expression network analysis identified two of nine modules (clusters of highly correlated proteins) to be significantly associated with chronological age and demonstrated that the biology of aging overlapped with complex age-associated diseases and other age-related traits. Pathway analysis showed that inflammatory response, organismal injury and abnormalities, cell and organismal survival, and death pathways were associated with aging.