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NIH grant 1R21AR082957-01A1: A deep phenotyping approach to characterizing osteoarthritis progression
Institute: {'code': 'AR', 'abbreviation': 'NIAMS', 'name': 'National Institute of Arthritis and Musculoskeletal and Skin Diseases', 'admin_org_id': '12500006', 'admin_funding_url': ''}. PI: Shabana Amanda Ali. Award: $366988.
Start: 2026-09-18T00:00:00.
SUMMARY: Osteoarthritis (OA) is a chronic, disabling condition affecting 7% of the global population, yet no
disease-modifying OA drugs (DMOADs) are approved. Identifying patients at high risk of OA progression would
enable targeted interventions to be developed to reduce the disease burden. Unfortunately, previous efforts to
characterize OA progression have been based on either symptoms (e.g., pain, stiffness) or structure (e.g.,
cartilage loss, bone remodeling), but typically not both. A major challenge to consolidating these features is their
discordance in OA over time. Additionally, environmental factors such as physical activity can impact OA
progression, but have yet to be considered in phenotyping efforts. This points to an urgent need for novel
methods to integrate multiple clinical factors into a unified OA phenotype that can be used for DMOAD discovery.
To meet this need, we propose a deep phenotyping approach that integrates symptoms, structure, physical
activity, and their changes over time using Bayesian statistical modeling to define a latent quantitative trait (LQT)
that represents OA progression phenotypes. This LQT score will be used to define individuals by their risk for
OA progression, objectively assessed by the need for surgery. It will also be used to identify microRNAs – small
non-coding RNA molecules – that could serve as biomarkers and mechanistic drivers for the corresponding
phenotype. Since microRNAs regulate gene expression and can be readily modulated in vivo, they represent
potential therapeutic targets. Similar Bayesian modeling approaches have been successfully applied in other
areas (e.g., cancer genomics) to identify patient subtypes. As preliminary data, we developed a LQT integrating
OA symptom and structure changes over 8 years, and show individuals who go on to have surgery have higher
LQT scores over time. Our primary objectives are to: i) integrate longitudinal symptom, structure, and
environmental data into a LQT that represents OA progression phenotypes, and ii) identify clinical outcomes
(i.e., surgery) and epigenetic factors (i.e., microRNAs) that are associated with the LQT. Our approach leverages
the Osteoarthritis Initiative (OAI), a longitudinal cohort of 4,129 participants with or at risk for knee OA that
includes data on structure (e.g., joint space), symptom (e.g., pain), and environmental (e.g., physical activity)
factors. We hypothesize that defining a comprehensive OA progression phenotype with a LQT will enable more
sensitive analyses of clinical outcomes and epigenetic factors than other uni/multivariate clustering methods.
This is significant because microRNAs are epigenetic factors that can be therapeutically targeted to enable
tailored intervention for OA. Our specific aims are to: 1) expand our LQT to capture structure, symptom, and
environmental factors over time to describe an OA progression phenotype; and 2) identify microRNAs associated
with the LQT as potential biomarkers and mechanistic drivers of OA. The LQT is expected to represent validated
OA progression phenotypes with both clinical and biological relevance. MicroRNAs associated with the LQT are
expected to foster translational research aimed at improving OA prognostication and DMOAD development.document fetched 2026-09-27 02:45