Abstract
Chronic pain is a complex and multifaceted disease, with biological causes distributed across tissues and bodily systems and influenced by psychological and social factors. A major aim of pain research is to find better ways to measure signals associated with chronic pain and thus predict, track, and treat pain conditions. Emerging biological measures related to the metabolic, immune, and nervous systems hold promise for identifying the physiology underlying pain and developing new treatments. Developing such measures will require multimodal datasets from large samples in combination with methodological advances. The Acute to Chronic Pain Signatures (A2CPS) Program serves as a model for such an effort, and its data will provide investigational opportunities for years to come. Acute to Chronic Pain Signatures is collecting multiomics, psychosocial, functional, and neuroimaging data from 2800 individuals before and after thoracic or knee replacement surgery. In addition to evaluating the prognostic value of previously identified biomarkers for predicting the transition to chronic pain, A2CPS will use machine learning and artificial intelligence to link these multiple data types and identify multimodal biosignatures of future pain. Open sharing of large datasets like that of the A2CPS, and models derived from them, will accelerate discovery and allow researchers to model brain, metabolic, and immune relationships relevant for multiple facets of health.