Predicting disease progression

(L to R) Drs. Shabana Amanda Ali, Osvaldo Espin-Garcia and Mohit Kapoor were authors on the study out of UHN’s Schroeder Arthritis Institute. (Photo: UHN StRIDe Team)

Researchers at University Health Network’s Schroeder Arthritis Institute have identified circulating molecules that can differentiate between patients with fast-progressing, slow-progressing and stable knee osteoarthritis.

Osteoarthritis is a degenerative joint disease that affects 50 per cent of individuals over the age of 65. Joint deterioration can progress quickly or slowly, but clinicians have no way of knowing in which individuals the disease will rapidly worsen.

“We previously identified circulating microRNAs – small molecules that alter gene expression – that contribute to joint damage in osteoarthritis,” says Dr. Mohit Kapoor, Co-Director and Senior Scientist at the Schroeder Arthritis Institute and senior author of the study.

“These molecules have great promise as predictive markers of disease progression because they can be easily detected in patient’s blood before the onset of advanced joint damage,” adds Dr. Kapoor, who is also a Professor in the Departments of Laboratory Medicine and Pathobiology, and Surgery at the University of Toronto (U of T).

Dr. Kapoor’s team examined the expression of microRNAs in blood samples from more than 100 patients in the Osteoarthritis Initiative – a large cohort of patients with knee osteoarthritis for which scientists have collected biospecimens and clinical data over at least 10 years of disease progression.

Osteoarthritis can range from mild to severe and the rate of disease progression varies between people. Molecules called microRNAs may help to predict how quickly a patient's disease will progress. (Photo: Getty Images)
Osteoarthritis can range from mild to severe and the rate of disease progression varies between people. Molecules called microRNAs may help to predict how quickly a patient’s disease will progress. (Photo: Getty Images)

The researchers analyzed blood samples that were collected at baseline, when patients had mild osteoarthritis, and four years later. They categorized the patients into three groups based on how quickly their disease progressed: fast progressors, who exhibited moderate to severe osteoarthritis by the four-year follow-up; slow progressors, who experienced worsening disease by the eight-year follow-up; and non-progressors, who did not experience worsening symptoms over the study period.

Using this approach, the team identified several circulating microRNAs that are associated with fast-progressing disease.

“We found that patients with fast-progressing disease had higher levels of 48 different microRNAs at baseline compared to patients with slow- and non-progressing disease,” explains Dr. Osvaldo Espin- Garcia, a principal biostatistician at the Schroeder Arthritis Institute and co-first author of the study.

“Among these were members of the microRNA-320 family, which target genes that contribute to inflammation and joint damage,” adds Dr. Espin-Garcia, who is also an Assistant Professor at U of T’s Dalla Lana School of Public Health.

This finding suggests that elevated levels of these microRNAs in a patient’s blood may signal rapid osteoarthritis progression. Next steps for this research include characterizing how these microRNAs contribute to the disease and determining if they can serve as targets for new therapies.

“Our study has revealed that molecular markers can predict whether a patient will experience fast- or slow-progressing knee osteoarthritis,” says Dr. Shabana Amanda Ali, a former postdoctoral fellow in Dr. Kapoor’s lab and co-first author of the study. “Clinicians could eventually screen patients for these markers to identify those at the greatest risk of disease progression.

“This would open a new treatment window during which patients could be prescribed preventative interventions or be enrolled into clinical trials for promising experimental therapies,” says Dr. Ali, who is also assistant scientist at Henry Ford Health.

This work was supported in part by UHN Foundation donors.

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