Plasma-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems

Aging is a complex process manifesting at the molecular, cell, organ and organismal levels. It leads to functional decline, disease and ultimately death, but the relationship between these fundamental biomedical features remains elusive. By applying machine learning to plasma proteome data of over fifty thousand human subjects in the UK Biobank and other cohorts, we report organ-specific and conventional aging models trained on chronological age, mortality and longitudinal proteome data. We show how these tools predict organ/systems-specific disease through numerous phenotypes. We find that men are biologically older and age faster than women, that accelerated aging of organs leads to diseases in these organs, and that specific diets, lifestyles, professions and medications are associated with accelerated and decelerated aging of specific organs and systems. Altogether, our analyses reveal that age-related chronic diseases epitomize accelerated organ- and system-specific aging, modifiable through environmental factors, advocating for both universal whole-organism and personalized organ/system-specific anti-aging interventions. ### Competing Interest Statement L.J.E.G and V.N.G. are the inventors on a U.S. Patent Application related to this work. ### Funding Statement This work was funded by the National Institute on Aging and Hevolution Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This work does not need any approval of the IRB. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The sensitive personal-level UK Biobank data used in this research cannot be made not openly available. However, all bone fide researchers who wish to conduct health-related research can [apply for access][1] to UK Biobank through UK Biobank’s access management system. [Filbin et al. (2021)][2] made their data openly available at Mendeley Data (DOI: [10.17632/nf853r8xsj.2][3]). [Dammer et al. (2022)][4] made their data openly available through the Synapse collaborative research platform at . The [Damsky et al. (2022)][5] data is available from the Gene Expression Omnibus (GEO) under identifier [GSE169148][6]. The proteomics data from the MESA study can be obtained from dbGaP under identifier [phs001416.v3.p1][7]. The coefficients from our conventional and organ-specific models are available in the supplementary tables. [1]: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access [2]: #ref-10 [3]: https://doi.org/10.17632/nf853r8xsj.2 [4]: #ref-6 [5]: #ref-7 [6]: https://www.ncbi.xyz/geo/query/acc.cgi?acc=GSE169148 [7]: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001416.v3.p1