# Structural, Biochemical and Functional Connectivity in Osteoarthritis using Quantitative Magnetic Resonance Imaging and Skeletal Biomechanics

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $702,738

## Abstract

Osteoarthritis (OA), a multifactorial disease that causes joint degeneration, affects 27 million U.S.
adults, and often leads to severe disability. The prevalence of OA is 33.6% in adults older than 65 years.
Despite the fact that OA is a widespread and debilitating disease, treatment options are currently extremely
limited, and established disease-modifying therapies do not exist. The solution generally offered to treat late
stage, symptomatic knee OA is joint replacement (total knee/hip arthroplasty, TKA/THA), and while this
surgically invasive and expensive remedy offers temporary relief, replacements often fail after 10-15 years,
with shorter life spans in obese individuals. In order to reduce the number of TKA/THA procedures, preventive
efforts and interventions targeting early stage OA are essential – the first step would be identifying subjects at
high risk for disease development, at a stage when tissue is not yet lost, and cartilage matrix abnormalities are
potentially reversible. Understanding the complex pathophysiology of joint degeneration, knee and hip joint
interactions, impact of gait biomechanics, are all critical to determine the mechanistic basis of hip OA.
 Hip OA progression marked by changes in cartilage biochemistry has complex multi-joint interactions
with several mechanistic factors, including morphological features, gait biomechanics and demographics.
However, studies to date have mostly focused on the relationship between single mechanistic factors and
semi-quantitative/ quantitative imaging measures, gait biomechanics, or non-objective symptomatic evidence
of hip OA. The overall goal of this competitive renewal is to extend our longitudinal work to determine the
structure-function-connectivity and mechanistic factors that mediate biochemical degeneration of hip cartilage
associated with OA progression over the mid-term (6-8-year) period.
 In this proposal, subjects will be recruited for a longitudinal study (covering 5-8 year follow up) from our
existing cohort (n=184 hips, 92 subjects), with the availability of existing 3-year follow-up data including bi-
lateral hip radiographs and MR images, gait biomechanics and patient reported outcomes. We will use novel,
fully automated and translational methods to measure MRI-based T1ρ and T2 relaxation time on a voxel basis,
which will provide precise and localized information on cartilage proteoglycan and collagen integrity and water
content. Automatic segmentation techniques combined with machine learning algorithms will be used to
analyze cartilage T1ρ/T2 in a well-characterized dataset with quantifiable T1ρ/T2 values on a voxel-level, and to
automatically detect the patterns of focal T1ρ/T2 elevations that lead to later stage OA and drive progression of
degenerative disease (over 8 years). Detailed analysis of gait biomechanics using functional principal
component analysis will utilize the rich skeletal biomechanics data and imaging (over 8 years). Imaging the
knee and hip...

## Key facts

- **NIH application ID:** 10666503
- **Project number:** 5R01AR069006-08
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Sharmila Majumdar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $702,738
- **Award type:** 5
- **Project period:** 2016-07-14 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10666503

## Citation

> US National Institutes of Health, RePORTER application 10666503, Structural, Biochemical and Functional Connectivity in Osteoarthritis using Quantitative Magnetic Resonance Imaging and Skeletal Biomechanics (5R01AR069006-08). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10666503. Licensed CC0.

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