A Data-Driven Biopsychosocial Framework Determining the Spreading of Chronic Pain

Author:

Tanguay-Sabourin ChristopheORCID,Fillingim MattORCID,Parisien MarcORCID,Guglietti Gianluca VORCID,Zare Azin,Norman Jax,Da-ano RonrickORCID,Perez JordiORCID,Thompson Scott JORCID,Martel Marc OORCID,Roy Mathieu,Diatchenko LudaORCID,Vachon-Presseau EtienneORCID

Abstract

AbstractChronic pain conditions are complex syndromes characterized by a mosaic of biological, psychological, and social factors. We derived predictive models for the number of co- existing pain sites in the UK Biobank and identified a common risk score that classified different chronic pain conditions in cross-sectional data, predicted the development of chronic pain in pain-free individuals, and determined the spreading of chronic pain to multiple sites or its recovery nine years later. The features with the strongest prognosis included sleeplessness, feeling ‘fed-up’, tiredness, stressful life events, and a BMI > 30. The risk score for pain was associated with an inflammatory blood marker, a polygenic risk score for pain, and a neuroimaging-based marker for sustained pain. The demonstration of a common biopsychosocial risk factor for different clinical pain conditions may help better characterize a general chronic pain syndrome, tailor research protocols, optimize patient randomization in clinical trials, and improve pain management.

Publisher

Cold Spring Harbor Laboratory

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