Osteoarthritis (OA) affects a staggering 500 million people around the world.
This painful and debilitating degenerative disease occurs when cartilage in the joints wears away, with the knee being the most commonly affected joint.
When standard treatments stop working, the only remaining option for those with knee OA (KOA) is knee replacement surgery — called a total knee arthroplasty, or TKA.
Sadly, up to one-third of patients experience no relief from this surgery.
In a new study from UHN’s Schroeder Arthritis Institute, a team led by Dr. Mohit Kapoor, Senior Scientist and co-senior author, has developed the first method of successfully classifying KOA patients using multiple “omics domains” — also known as multi-omics — to explain differences in post-TKA outcomes.
Using multi-omics and artificial intelligence (AI) models, researchers were able to analyze to multiple sets — domains — of biological molecules simultaneously and investigate connections between them that could not have been in previous research, which looked only at individual domains.
The Schroeder team’s breakthrough classification method, which is also called a model, is composed of two novel algorithms — one using an AI-based deep-learning technique to identify subtypes of KOA patients — called endotypes — and one using machine learning (ML) to analyze differences between patients within each endotype.
"Our model uncovered three novel KOA endotypes and identified key features that likely contribute to differences in patients’ responses to TKA,” says Dr. Divya Sharma, Senior Biostatistician at UHN’s Princess Margaret Cancer Centre, an assistant professor at York University and study co-first author.
‘Excited by the possibility of models like ours’
The study further revealed that the factors contributing to whether a patient experiences pain post-TKA are unique to each KOA endotype — as are the molecular pathways responsible for the development of this disease in the first place.
“Beyond deepening our understanding of what underlies KOA and differences in patients’ responses,” says Dr. Jason Rockel, a staff scientist at Schroeder Arthritis Institute and co-first author of the study. “We are excited by the possibility of models like ours helping to transform how treatment decisions are made.”
Predictive models could be a tool for health care providers to determine whether a particular treatment will benefit a patient before they decide to offer it.
“Such a tool would streamline the process of finding an effective treatment for an OA patient by eliminating the need for trial and error, ultimately preventing patients from undergoing invasive treatments with long recovery processes, like TKA, without the promise of success,” says Dr. Rajiv Gandhi, clinician-scientist at Schroeder Arthritis Institute, associate professor in the Department of Laboratory Medicine and Pathobiology at the University of Toronto and co-senior author of the study.
By making their data and model publicly available, the team hopes that the greater OA research community will contribute to an even deeper understanding of this debilitating disease.
While this is only the first iteration, with further refinement, a tool like the model in this study has the potential to be used in clinics and clinical trials for the KOA community and beyond.
Read more about the study, which was supported by the Canada Research Chairs Program, Tony and Shari Fell Platinum Chair in Arthritis Research, Campaign to Cure Arthritis, and UHN Foundation.
