Oh HS, Rutledge J, Nachun D, Pálovics R, Abiose O, Moran-Losada P, Channappa D, Urey DY, Kim K, Sung YJ, Wang L, Timsina J, Western D, Liu M, Kohlfeld P, Budde J, Wilson EN, Guen Y, Maurer TM, Haney M, Yang AC, He Z, Greicius MD, Andreasson KI, Sathyan S, Weiss EF, Milman S, Barzilai N, Cruchaga C, Wagner AD, Mormino E, Lehallier B, Henderson VW, Longo FM, Montgomery SB, Wyss-Coray T. Organ aging signatures in the plasma proteome track health and disease. Nature. 2023 Dec;624(7990):164-172. Epub 2023 Dec 6 PubMed.
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NIH-NIA
This is wonderful work. It truly lays out the basis for translation to the clinic of plasma proteomic analysis, a powerful method that so far has been used almost exclusively for research purposes (some application in oncology excluded).
The authors measure a large number of proteins (~5k), in plasma samples of five separate cohorts, using an aptamer technology. From the proteins assessed and quantified, they select a subgroup that is specifically overrepresented in specific organs. They do this using data from a high-quality database (GTEX) of gene expression from specific organs.
Then, using organ-specific subsets, they derive organ-specific aging clocks, and then they go on to demonstrate that these organ-specific aging clocks specifically predict emerging pathology in those organs. The specificity and predictive value obtained are impressive and appear to be superior to some of the biomarkers used currently in clinical practice.
Overall, all the organ-specific clocks predict mortality, although with different strength. Only a minority of individuals (<2 percent) appear to have accelerated “aging” in multiple organs. This is somewhat surprising because previous studies have found that multiple organ function and rate of decline with aging are correlated, especially in older persons.
The analysis of the brain-specific clock is particularly elaborated and sophisticated and, to work properly, required tuning a clock on both organ-specific and chronological age. After this analysis, the predictivity of various brain-specific phenotypes is remarkable.
View all comments by Luigi FerrucciNational Institute on Aging
In a commendable effort, Oh and colleagues identified a set of organ-specific proteins that were then used to compute organ-specific age and age gaps. By taking a set of proteins that were specifically expressed in brain tissue and mapped onto cognitive function, the authors identified a brain-specific proteomic signature that was associated with AD dementia.
The protein-based organ age scores could provide potential value for both research and clinical practice. For example, in addition to providing individuals with a readout of organ-specific health, the organ age scores will likely be useful for monitoring the therapeutic or adverse effect of intervention or exposures on multiple organ systems in a cost-effective manner.
Beyond demonstrating that the molecular brain aging signature is associated with AD dementia, the authors identified a subset of brain-specific proteins as part of the cognition-optimized brain aging model. This subset of proteins was used to calculate the CognitionBrain age gap, a measurement that proved to be much more predictive of AD than the Brain age gap.
Importantly, the authors show that the CognitionBrain age gap predicted AD risk independent of plasma pTau181, age, and AD polygenic risk score. These findings suggest that the CognitionBrain age gap may provide incremental value for dementia prediction beyond that of traditional AD/dementia biomarkers. Proteins that had the largest positive weights for the CognitionBrain age score included several synaptic proteins, such as complexin 1 (CPLX1) and complexin 2 (CPLX2), which my group recently identified as midlife biomarkers of 25-year dementia risk (Walker et al., 2023).
Of particular importance is the set of analyses that examined how non-CNS organ-specific aging related to cognitive decline and Alzheimer’s disease. By demonstrating that the age gaps derived from artery- and pancreas-specific proteins and optimized for cognition predicted AD risk, the authors provide evidence for the role of these organ systems in AD/dementia pathogenesis. Early vascular dysfunction was especially implicated in this study, as the artery age gap predicted conversion to MCI over a 15-year follow-up period and did so more strongly than did brain age. These findings support previous work that has identified vascular dysfunction as an early feature of—and likely risk factor for—late-onset AD (Iturria-Medina et al., 2016).
References:
Walker KA, Chen J, Shi L, Yang Y, Fornage M, Zhou L, Schlosser P, Surapaneni A, Grams ME, Duggan MR, Peng Z, Gomez GT, Tin A, Hoogeveen RC, Sullivan KJ, Ganz P, Lindbohm JV, Kivimaki M, Nevado-Holgado AJ, Buckley N, Gottesman RF, Mosley TH, Boerwinkle E, Ballantyne CM, Coresh J. Proteomics analysis of plasma from middle-aged adults identifies protein markers of dementia risk in later life. Sci Transl Med. 2023 Jul 19;15(705):eadf5681. PubMed.
Iturria-Medina Y, Sotero RC, Toussaint PJ, Mateos-Pérez JM, Evans AC, Alzheimer’s Disease Neuroimaging Initiative. Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis. Nat Commun. 2016 Jun 21;7:11934. PubMed.
View all comments by Keenan WalkerIcahn School of Medicine at Mount Sinai
This new study provides one of the most comprehensive approaches to date on how aging intersects with many disorders for which it acts as a major risk factor. One major strength is Oh et al.'s leveraging of a large-scale protein quantification approach with existing gene expression datasets to create fresh insight. Aptamer-based relative quantification of nearly 5,000 plasma proteins was performed in more than 5,000 individuals from cohorts across several U.S. centers.
To gain organ specificity for the plasma proteins, the authors mapped these proteins to organ-specific gene expression enrichments gleaned from the GTEx project. This allowed organ age gaps in biological and chronological age to be determined for each organ under study using machine-learning approaches. Interestingly, while there are extreme organ age gaps that associate with some diseases, Alzheimer’s disease most strongly associates with the overall set of organismal aging proteins in this model, suggesting that AD may be a disorder involving multiple organ systems.
A wealth of information from the identified plasma protein organ-specific sets can be mined to further probe specific drivers for aging processes not previously linked to certain diseases. To extract additional insights linking brain aging to AD specifically, the authors used cognitive phenotypes in their cohorts and brain age gap protein sets to train a more sophisticated model they term the CognitionBrain aging model. This brain age gap model was able to provide predictive power for several important AD-related measures. Several of the individual proteins comprising the model were previously associated with cognition and AD. Of note, some are enriched in oligodendrocytes, a cell type shown to be sensitive to the effects of young blood in old mice through parabiosis (Ximerakis et al., 2023).
Of particular interest to my group is the subset of brain proteins in the model involved in the biology of the extracellular matrix, which we find is regulated by certain systemic mediators in the context of brain aging (Castellano et al., 2017; Ferreira et al., 2023).
Overall, the study sets up many exciting directions for the field. How malleable are the aging signatures for specific organs in the face of challenges, lifestyle changes, or therapeutic interventions? Are some organs more resistant to aging reversal than others? With the relative ease of longitudinal blood draws, these and many other answers seem within reach, especially with the ever-expanding coverage offered by proteomic platforms and development of new computational approaches to model the complexity of aging.
References:
Ximerakis M, Holton KM, Giadone RM, Ozek C, Saxena M, Santiago S, Adiconis X, Dionne D, Nguyen L, Shah KM, Goldstein JM, Gasperini C, Gampierakis IA, Lipnick SL, Simmons SK, Buchanan SM, Wagers AJ, Regev A, Levin JZ, Rubin LL. Heterochronic parabiosis reprograms the mouse brain transcriptome by shifting aging signatures in multiple cell types. Nat Aging, March 9, 2023
Castellano JM, Mosher KI, Abbey RJ, McBride AA, James ML, Berdnik D, Shen JC, Zou B, Xie XS, Tingle M, Hinkson IV, Angst MS, Wyss-Coray T. Human umbilical cord plasma proteins revitalize hippocampal function in aged mice. Nature. 2017 Apr 19; PubMed.
Ferreira AC, Hemmer BM, Philippi SM, Grau-Perales AB, Rosenstadt JL, Liu H, Zhu JD, Kareva T, Ahfeldt T, Varghese M, Hof PR, Castellano JM. Neuronal TIMP2 regulates hippocampus-dependent plasticity and extracellular matrix complexity. Mol Psychiatry. 2023 Sep;28(9):3943-3954. Epub 2023 Nov 2 PubMed.
View all comments by Joseph CastellanoUniversity of Kansas
This study provides nice insights into aging at the molecular level, gained through proteomics applications. That the investigators were able to generate such a powerful dataset, and analyze the mass of data, shows how far proteomics technology and machine learning have truly come. The ongoing maturation of the approaches used here will advance our knowledge of aging.
Though the analyses were unbiased and agnostic, the interpretation of their place in aging and biology are speculative but reasonable. Overall I tend to agree with the authors’ interpretations. Some of the speculative points, if accurate, to me pack quite a wallop.
One such point had to do with the fact that while a large number (~20 percent) had disproportionate aging in one organ, far fewer (<2 percent) had disproportionate aging in more than one organ. Adding to this additional context that disproportionate organ aging predicts organ-related disease, I wonder whether a particular stress within an organ accelerates the subsequent aging of the organ.
If one extrapolates from data that indicate advancing age demands functional compensation, one could wonder whether an organ-specific stress compounds the amount of compensation that is triggered by aging itself. This might force the organ to reach its limit of compensation, and to transition from compensated to decompensated aging.
The manuscript identifies proteins and, by extension, pathways that I suspect will provide insight into the aging-Alzheimer’s disease nexus. Studies like this one, of course, are correlation-focused, and inferring causation from correlation is tricky. Does disease/stress cause aging, or does aging cause disease/stress? Maybe it is both ways.
Also, while I was very impressed that the authors were able to infer organ specificity of the measured plasma proteins, I wonder how confident we can be about the origin of those proteins. To this point, the manuscript briefly addresses the issue of protein levels going in opposite directions between brain and plasma, and gives the example of Aβ levels going down in Alzheimer’s patient CSF while cortex plaque burden increases. Explanations for this general principle, as is the case with Aβ compartmentalization, may be more complex than we imagine.
Regardless, this is a very well-done and interesting study.
View all comments by Russell SwerdlowMake a Comment
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