Creation of Dynamic, Non-Stochastic, W Function Informed Methylation Risk Scores (MRS) for Diagnostic/Therapeutic Applications

Current disease diagnostic methods for chronic diseases rely heavily on tools that capture stochastic and causal biological states, failing to capture the dynamic nature of the diseases. The proposed project aims to develop a groundbreaking solution by creating a non-stochastic "W Function Epigenomic Roadmap" using systems-based dynamical modeling to address these limitations. Unlike causal and stochastic measures, this algorithm will systematically quantify the dynamic interactions of epigenetic markers over time using longitudinal data. TruDiagnostic is uniquely positioned to lead this effort due to its established expertise in epigenetic testing, proprietary algorithms, and a robust network of healthcare providers. The project’s high-level aims include (1) developing a network of epigenetic markers based on the W Function, focusing on shifts in homeostatic set points over time; (2) validating these findings in two chronic disease models and mortality to understand how stability in epigenetic networks is altered in disease states; and (3) assess whether our novel algorithms are able to capture more dynamic changes associated with treatments and interventions compared to traditional DNAm algorithms. The expected outcome is a transformative diagnostic tool that will allow for personalized, dynamic health forecasts and identification of critical points for early intervention, offering new pathways for treatment and improving health outcomes across chronic diseases. Project Number: 1R43AG097165-01 | Fiscal Year: 2025 | NIH Institute/Center: National Institute on Aging (NIA) | Principal Investigator: Varun Dwaraka | Institution: TRU DIAGNOSTICS, INC., LEXINGTON, KY | Award Amount: $320,511 | Activity Code: R43 | Study Section: Special Emphasis Panel[ZAG1 ZIJ-T (M3)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11253983