In Nature, a team has published its research on using protein analysis to estimate the aging of specific organs.
Aging is not all at once
While the processes of aging affect every organ of every person, the rates at which these processes affect these organs can vary greatly in people [1] and mice [2]. Similar, rejuvenative interventions, such as heterochronic parabiosis, do not affect all cell types in the same way [3]. The molecular mechanics behind these differences, and organ aging more generally, remain only partially explained.
While previous research has been conducted on measuring individual organ aging [4], these researchers note that most of these methods are not very specific. Some methods that are specific to individual organs, such as the brain, either lack protein-related information [5] or require tissue samples that make them inappropriate for use in living people [6].
A protein approach
The authors of this paper noted that organs release specific proteins into the blood plasma. Doctors already analyze these proteins to diagnose specific diseases, such as liver damage. They hypothesized that a more detailed understanding of these plasma proteins might allow for their use as biomarker of aging.
To test their hypothesis, they used almost 5,000 different proteins derived from more than 5,000 people in five separate cohorts. They considered a protein to be specific to an organ only if it was expressed four times more in that organ than in any other organ. A total of 893 of the studied proteins met that criterion. These proteins, along with the less-specific proteins, were used to train chronological age predictors.
Comparing general to organ-specific aging yielded some surprising results. Whole-organism aging and individual organ aging were only loosely correlated. Nearly 20% of people across the five cohorts were extreme agers in one specific organ. Only 1.7% of the people in these cohorts had extreme aging in multiple organs. Of course, as with any age-related cohort study, survivorship bias may have played a role in these findings.
The relationship to disease
The researchers then checked to see if people who had experienced extreme aging had known disease states. Kidney aging was found to be correlated with the various diseases associated with metabolic syndrome: diabetes, obesity, hypertension, and excessive cholesterol. The longevity-related protein klotho was identified as being related to kidney aging.
Unsurprisingly, heart aging was associated with the two heart diseases mentioned: atrial fibrillation and heart attacks. In people with no abnormalities at baseline, a single standard deviation of 4.1 years of additional heart age multiplied the risk of heart disease by 2.5 over 15 years. MYL7, a protein that has been investigated as a target for the treatment of cardiomyopathy [7], was one of the associated proteins.
Muscle aging was related to gait impairment, which suggests sarcopenia, and aging of the brain was related to problems with its vascular system. Interestingly, whole-body aging was more correlated with Alzheimer’s disease than brain aging was.
While many of the proteins involved in this study had previously been associated with aging and disease, many had not. Controlling for previously established proteins demonstrated that this model makes predictions that are more effective than those proteins alone.
Effective mortality predictors
Mortality risk increases of 15% to 50% were associated with a standard deviation increase in the aging of individual organs, including adipose tissue, lung tissue, and pancreatic tissue. The researchers note that this accuracy is akin to methylation-based clocks, such as the mortality predictor GrimAge, despite organ-based clocks being geared towards chronological age assessment.
It was also possible to use a subset of proteins to assess the risk of Alzheimer’s disease, and the researchers developed a separate clock to do this. However, not all of the proteins associated with Alzheimer’s were associated specifically with the brain. Including arterial, whole-organism, and pancreatic proteins provided a more reliable metric, and these proteins were able to predict a transition from cognitive normalcy to mild cognitive impairment.
However, this is only an association study that does not prove causation. In many cases, it is not clear if the presence of a specific protein leads to organ aging, if organ aging is accelerated by its presence, or if there is a bi-directional relationship. It was also not clear if these findings apply to every organ in the body, and, although a linear method was used in this study, the researchers found evidence for nonlinear relationships.
Literature
[1] Hajat, C., & Stein, E. (2018). The global burden of multiple chronic conditions: a narrative review. Preventive medicine reports, 12, 284-293.
[2] Schaum, N., Lehallier, B., Hahn, O., Pálovics, R., Hosseinzadeh, S., Lee, S. E., … & Wyss-Coray, T. (2020). Ageing hallmarks exhibit organ-specific temporal signatures. Nature, 583(7817), 596-602.
[3] Conboy, I. M., Conboy, M. J., Wagers, A. J., Girma, E. R., Weissman, I. L., & Rando, T. A. (2005). Rejuvenation of aged progenitor cells by exposure to a young systemic environment. Nature, 433(7027), 760-764.
[4] Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., & Zalesky, A. (2023). Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nature Medicine, 29(5), 1221-1231.
[5] Cole, J. H., Ritchie, S. J., Bastin, M. E., Hernández, V., Muñoz Maniega, S., Royle, N., … & Deary, I. J. (2018). Brain age predicts mortality. Molecular psychiatry, 23(5), 1385-1392.
[6] Glorioso, C., Oh, S., Douillard, G. G., & Sibille, E. (2011). Brain molecular aging, promotion of neurological disease and modulation by Sirtuin5 longevity gene polymorphism. Neurobiology of disease, 41(2), 279-290.
[7] Saberi, S., Cardim, N., Yamani, M., Schulz-Menger, J., Li, W., Florea, V., … & Jacoby, D. (2021). Mavacamten favorably impacts cardiac structure in obstructive hypertrophic cardiomyopathy: EXPLORER-HCM cardiac magnetic resonance substudy analysis. Circulation, 143(6), 606-608.
