Deciphering Proteomic Aging: From Causal Mechanisms to Frailty Prediction.

/ABSTRACT Aging and frailty are major contributors to chronic diseases and multimorbidity, yet the causal mechanisms driving biological aging remain poorly understood. While recent advances in multi-omics research have led to the development of biological age clocks, it is unclear which components of these models represent causal drivers of age-related diseases and which are merely correlated with aging. Furthermore, while screening tools exist that can identify existing frailty, there is a strong unmet need for screening tools that can identify patients at high future risk of frailty so that they can be identified early and offered preventative care to promote healthy aging and resilience. Using proteomic-based approaches may be a particularly successful approach, but there exist no large studies using population data to develop proteomic prediction models of future frailty. To address these gaps, I propose to bring together large-scale genetic, multi-omic, and clinical data from the UK Biobank, Multi-Ethnic Study of Atherosclerosis (MESA), FinnGen, and All of Us (combined n=70,225) to investigate the biological underpinnings of proteomic aging and develop effective clinical prediction tools for frailty risk. Specifically, this project will: (Aim 1) use genetic association data from over 500,000 individuals to identify aging proteins with causal relationships to major chronic diseases using Mendelian randomization and colocalization, (Aim 2) map the upstream epigenetic and transcriptional regulation of these proteins, and (Aim 3) develop a proteomic model to predict future risk of frailty before its onset. This work will provide significant new knowledge about the causal proteins involved in age-related multimorbidity and the upstream molecular mechanisms that regulate proteomic aging to inform possible therapeutic targets, and will develop scalable and transferable proteomics-based tools for frailty prediction to promote health and resilience in aging populations. This K99/R00 award will build upon my previous research experience in computational proteomics and population health through its emphasis on providing training in five new domains: (1) statistical genetics, (2) conducting clinically-relevant research on frailty, (3) epigenomics and transcriptomics analysis, (4) research on aging, and (5) professional development. All research will be conducted in the Analytic and Translational Genetics Unit at Massachusetts General Hospital and the Broad Institute, with mentorship from renowned scientists Drs. Mark Daly, Douglas Kiel, and Vadim Gladyshev. Additional guidance from scientific advisor Dr. Benjamin Neale will ensure exceptional guidance and support. Overall, the training environment is outstanding, the mentors and advisors are world-class, and the proposed studies address an urgent unmet need. These skills are fundamental to my goal of establishing my own lab that conducts NIH-funded research studying the genomic and proteomic basis of human aging and develops effective screening tools for use in prevention of age-related diseases and promoting healthy aging and resilience among aging populations. Project Number: 1K99AG090628-01A1 | Fiscal Year: 2026 | NIH Institute/Center: National Institute on Aging (NIA) | Principal Investigator: Michael Argentieri | Institution: MASSACHUSETTS GENERAL HOSPITAL, BOSTON, MA | Award Amount: $134,726 | Activity Code: K99 | Study Section: Special Emphasis Panel[ZRG1 AN-N (80)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1K99AG09062801A1