Native American (NA) populations have higher rates of rheumatic disease and present with overlapping disease symptoms and nontraditional serological features, thus presenting an urgent need for better biomarkers in NA diagnostics. This study utilized a machine-learning approach to identify immune signatures that more effectively stratify NA rheumatic disease patients.
Adult NA patients with autoantibody-positive (AAB+) rheumatoid arthritis (RA) (n=28), autoantibody negative (AAB-) RA (n=18), systemic autoimmune rheumatic disease (n=28), arthralgia/osteoarthritis (n=28), polyarthritis/undifferentiated connective tissue disease (n=28), and controls (n=28) provided serum samples for cytokine, chemokine, and autoantibody assessment. Random Forest clustering and soluble mediator groups were used to identify patients and controls with similar biologic signatures. ACR criteria specific for systemic disease and RA identified differences in disease manifestations across clusters.
Serum soluble mediators were not homogenous between different NA rheumatic disease diagnostic groups reflecting the heterogeneity of autoimmune diseases. Clustering by serum biomarkers created five analogous immune phenotypes. Soluble mediators and pathways associated with chronic inflammation and involvement of the innate, B cell, Tfh cell, and IFN-associated pathways, along with regulatory signatures, distinguished the five immune signatures among patients. Select clinical features were associated with individual immune profiles. Subjects with low inflammatory and higher regulatory signatures were more likely to have few clinical manifestations, whereas those with T cell pathway involvement had more arthritis.
Serum protein signatures distinguished NA rheumatic disease patients into distinct immune subsets. Following these immune profiles over time may assist with earlier diagnoses and help guide more personalized treatment approaches.