Modeling osteoarthritis through biomechanics and imaging (MOBI)

OA is expected to be the most prevalent disease in the Netherlands by 2040. The
knee is the most common joint affected by OA, and knee OA is responsible for
more physical disability than any other disease among older adults.

Knee OA is predominantly held to be caused by pathomechanics: biomechanical
factors that lead to increases in cartilage stress, promoting its degeneration.
Current methods used to evaluate knee biomechanics contain substantial error,
which prevents accurate estimates of cartilage stress. A more accurate and
precise measure of stress distribution throughout the cartilage tissue (instead
of the average load through the joint) would improve our ability to detect and
quantify the relationships between joint/cartilage pathomechanics and OA.

This would have substantial impact on OA research and management by making it
possible to incorporate relevant biomechanical factors into personalized
prediction models. Moreover, it would inform hypotheses about which
interventions might favourably alter biomechanics, potentially leading to
improved outcomes including slowing down the progression of OA, reducing pain,
and improving function and quality of life.

We aim to develop a personalized multi-scale biomechanical model of the knee
that integrates joint movement (marker-based motion capture, fluoroscopy), EMG
and force data with quantitative cartilage parameters derived from advanced
imaging techniques, to yield precise and accurate measures of intra-articular
forces and tissue stress distributions. We will then evaluate the associations
between these measures and pathophysiological measures (e.g., subchondral bone
metabolism and structural OA features) derived from positron emission
tomography - magnetic resonance imaging (PET/MRI) over a 2-year period.

We will evaluate test-retest reliability of our biomechanical data collection
protocol and processing pipelines and assess the sensitivity of our models to
changes of input parameters. We will validate a MRI-based method of
image-registration against a CT-based method.

We will assess the correlation between subchondral bone metabolism and
structural progression of OA. Finally, we aim to explore associations between
our biomechanical parameters and other relevant variables including patient
demographics, physical examination tests, and self-reported outcomes like pain.
The purpose of these analyses will be hypothesis-generating in nature.

This study includes a technical development phase and a 2-year prospective
observational study.

Participants involved in the technical development will attend a single visit
involving biomechanical assessment, photon-counting CT (PCCT) and MR image
acquisition. They will be exposed to a maximum total radiation of 0.07 mSv.
Those in the prospective study will be physically assessed at baseline and
2-year follow-up and fill out questionnaires at 1-year follow-up.

The physical visits will include biomechanical assessment (performing walking
and lunging tasks), PET/MR image acquisition, physical examination, completion
of questionnaires, and blood draw. Total radiation exposure will not exceed
2.47 mSv. Time for data collection will be approximately 4-6 hours at baseline
and 2-year follow-up, divided into two 2-3-hour visits.

At the one year follow-up, questionnaires will take approximately 30 minutes to
complete, these will be performed remotely. In total, the time commitment for 4
visits plus completion of questionnaires at home will be 8-13 hours