Biological markers and psychosocial factors predict chronic pain conditions

Matt Fillingim (), Christophe Tanguay-Sabourin, Marc Parisien, Azin Zare, Gianluca V. Guglietti, Jax Norman, Bogdan Petre, Andrey Bortsov, Mark Ware, Jordi Perez, Mathieu Roy, Luda Diatchenko and Etienne Vachon-Presseau ()
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Matt Fillingim: McGill University
Christophe Tanguay-Sabourin: McGill University
Marc Parisien: McGill University
Azin Zare: McGill University
Gianluca V. Guglietti: McGill University
Jax Norman: McGill University
Bogdan Petre: Dartmouth College
Andrey Bortsov: Duke University
Mark Ware: McGill University Health Center
Jordi Perez: McGill University Health Center
Mathieu Roy: McGill University
Luda Diatchenko: McGill University
Etienne Vachon-Presseau: McGill University

Nature Human Behaviour, 2025, vol. 9, issue 8, 1710-1725

Abstract: Abstract 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. Here, in this multidataset study of over 523,000 participants, we applied machine learning to multidimensional biological data from the UK Biobank to identify biomarkers for 35 medical conditions associated with pain (for example, rheumatoid arthritis and gout) or self-reported chronic pain (for example, back pain and knee pain). 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). Notably, all biomarkers worked in synergy with psychosocial factors, accurately predicting both medical conditions (AUC 0.69–0.91) and self-reported pain (AUC 0.71–0.92). These findings underscore the necessity of adopting a holistic approach in the development of biomarkers to enhance their clinical utility.

Date: 2025
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DOI: 10.1038/s41562-025-02156-y

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