Research at Lund University in Osteoarthritis Development (ReLOAD)
A new creative research environment appointed and supported by the Medical Faculty, Lund University.
Our research will shed light on osteoarthritis aetiology and pathogenesis, treatments and burden on the individual and society. We study osteoarthritis, from molecular processes and biomechanics in the joint to public health, with the long term goal of identifying targets for therapy and implement methods of prevention. Thus, we will enable better health in the population which we will monitor by our register-based outcomes.
Our work packages (WPs):
WP 1: Epidemiology and evaluation of treatment strategies for osteoarthritis
We utilize unique registries (e.g., Skåne healthcare data) to gain new insights on current and future disease prevalence and consultation rates of joint injury and osteoarthritis. Continuous monitoring of disease occurrence makes it possible to track secular trends critical for understanding the disease aetiology and risk factors, and for enabling effective health care planning and resource prioritizing. By cross linking such data with other metadata from Statistics Sweden, the Swedish Board of Welfare and the Social Insurance Agency as well as with patient-relevant information collected from the Better management of Osteoarthritis (BOA) register, we determine effects and health care seeking patterns in the entire population before and after treatment initiation, as well as sick leave patterns for working-age individuals (Figure 1). Hence, we provide novel insights on the impact and the effects of treatments prescribed by health care professionals in daily clinical practice (i.e., outside the setting of a randomized clinical trial).
WP 2: Molecular changes related to osteoarthritis development after knee joint injury
We measure cytokine levels, protease activity and identify biological pathways that are involved in the degradation of extracellular matrix proteins with the goal to identify targets for pharmaceutical intervention. We use collected bio-specimen data (e.g., the LUMEN cohorts, and the KANON-trial) to study early and late stages of the disease. In KANON, a unique prospective randomized clinical trial comparing a surgical and non-surgical treatment strategy after acute anterior cruciate ligament injury, we follow the osteoarthritis disease from healthy joint to diseased joint. We have access to serial magnetic resonance images, bio-specimens (serum, urine and synovial fluid), radiographs and patient-reported outcomes over the first five years after injury.
We apply a model of cartilage injury to bovine cartilage to mimic inflammation in order to characterize events in the tissue to understand pathophysiology and identify relevant early molecular fragmentations. This will allow extensive identification of the molecular processes initiated that lead to tissue destruction. Developments of assays for fragments released and identified in this model will be applied to bio-specimens from the prospective studies and allow us to identify and understand critical early events. Until now a major deficiency in developing therapy has been the inability to identify an early disease stage suitable for intervention. Promising preliminary data shows that fragments found in human disease also can be obtained in the described model (Figure 2).
WP 3: Biomechanical effects in osteoarthritis and bone changes after joint injury
Bone is a living structure constantly adapting to changes in biomechanical loads. The two-dimensional (2D) bone texture provided by a plain radiography contains information directly related to the three-dimensional (3D) bone structure (Figure 3). Therefore, there is a growing interest in developing low-cost and non-invasive bone texture-based system for predicting osteoarthritis and its progression. Our methods are highly innovative and may be important future tools in the clinic to monitor and, already at an early stage, accurately predict the patient’s prognosis.
We develop methods that will improve diagnosis of osteoarthritis by accounting for and predicting geometrical features that are related to the disease. One of our methods is based on integrating functional imaging of the bone based on radiographs with statistical shape modelling using an active appearance model. From a 2D radiographic image, the method estimates the 3D geometry and internal structure of the bone. This allows quantification of geometrical shape parameters and how these parameters relate to development of osteoarthritis. The method is primarily developed and validated in the hip joint, but will be translated to the knee. We have shown that the 3D geometry of the femur can be approximated from 2D images.
We further examine the size, shape, and orientation of trabecular bone in knees and hands foreseeing the risk for structural progression of osteoarthritis in close collaboration with biomechanical engineers at Curtin University, Australia.