A Biomarker-Based Framework for the Prediction of Future Chronic Pain
Chronic pain is a multifactorial condition presenting significant diagnostic and prognostic challenges. Biomarkers for the classification and the prediction of chronic pain are therefore critically needed. In this multi-dataset study of over 523,000 participants, we applied machine learning to multi-dimensional biological data from the UK Biobank to identify biomarkers for 35 medical conditions associated with pain (e.g., clinical diagnosis of rheumatoid arthritis, fibromyalgia, stroke, gout, etc.) or self-reported chronic pain (e.g., back pain, knee pain, etc). Biomarkers derived from blood immunoassays, brain and bone imaging, and genetics were effective in predicting medical conditions associated with chronic pain (area under the curve (AUC) 0.62-0.87) but not self-reported pain (AUC 0.50-0.62). Among the biomarkers identified was a composite blood-based signature that predicted the onset of various medical conditions approximately nine years in advance (AUC 0.59-0.72). Notably, all