Individualized Medicine and Division of Rheumatology have developed a first-of-its-kind machine learning algorithm that can predict rheumatoid arthritis disease activity in a patient. The algorithm analyzes biochemical metabolites ― the product of the body's metabolism ― in blood.

"Having fast, reliable and scalable measures for predicting the clinical course of disease activity is an important unmet need for patients with rheumatoid arthritis,'' says Jaeyun Sung, PhD, a computational biologist within the Center for Individualized Medicine and co-senior author of the study. Dr. Sung develops computational analytical approaches to understand the intricate relationship between microbial organisms and human metabolic and immune health. 

The study, which was published in Arthritis Research & Therapy, lays the groundwork for monitoring rheumatoid arthritis disease progression and systemic inflammation using blood samples alone. The findings provide direction for the potential future development of clinical laboratory tests and digital diagnostics to further enable precision medicine for rheumatoid arthritis patients.

Rheumatoid arthritis is a chronic, autoimmune disorder characterized by joint inflammation and pain that can eventually lead to bone and cartilage erosion, joint deformity, and loss in mobility. This complex disease affects nearly 1.3 million people in the U.S.

Dr. Sung says the study sheds light on why symptoms differ significantly among rheumatoid arthritis patients, which in turn makes it is so difficult to treat.

"We turned to the blood because it could potentially provide a treasure-trove of novel biomarkers for assessing not only disease activity, but also clinical subgroups, risk factors and predictors of treatment response that complement current standard laboratory tests," Dr. Sung explains.

John Davis III, MD, a clinical rheumatologist in Mayo Clinic's Division of Rheumatology with a specialty interest in inflammatory arthritis, says a patient's rheumatoid arthritis disease activity is not notable through symptoms alone. He says providing interpretable predictions could enhance the clinical treatment of rheumatoid arthritis. Dr. Davis is co-senior author of the study.

"Our study highlights the importance of investigating which biochemical functions are altered during the onset and progression of the disease," Dr. Davis says. "To this end, metabolomics platforms can present unique opportunities for discovering novel biomarkers."