Creation of dynamic, non-stochastic, W function informed methylation risk scores (MRS) for diagnostic/therapeutic applications

TRU Diagnostics Inc. received a $320,511 Project Grant from the National Institute on Aging (NIA) under the Aging Research program (CFDA 93.866), awarded September 20, 2025, with a completion date of September 19, 2026. The project involves development of novel methylation risk scores (MRS) based on a non-stochastic W function epigenomic framework designed to improve diagnostic capabilities for chronic diseases. The deliverables include creation of a dynamic epigenetic marker network that identifies shifts in homeostatic set points over time, validation of these findings in two chronic disease models and mortality outcomes, and comparative assessment of the company's proprietary algorithms against traditional DNA methylation (DNAm) algorithms to determine their ability to capture treatment-related and intervention-related changes.

The primary product emerging from this one-year project is a transformative diagnostic tool enabling personalized, dynamic health forecasts and identification of critical intervention points for chronic disease management. Unlike existing diagnostic methods that rely on stochastic and causal biological measurements, this systems-based dynamical modeling approach leverages longitudinal epigenomic data to quantify how epigenetic stability becomes altered in disease states. TRU Diagnostics, leveraging its established expertise in epigenetic testing and proprietary algorithms alongside its network of healthcare providers, is positioned to deliver a solution with potential applications for improved health outcomes across chronic disease populations.

Abstract

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.