| Title: | Biological markers and psychosocial factors predict chronic pain conditions |
| Journal: | Nature Human Behaviour |
| Published: | 12 May 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40355673/ |
| DOI: | https://doi.org/10.1038/s41562-025-02156-y |
| URL: | https://www.nature.com/articles/s41562-025-02156-y.pdf |
Publication 15361
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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.</p>
11 Keywords
- Adult
- Aged
- Biomarkers
- Chronic Pain
- Female
- Humans
- Machine Learning
- Male
- Middle Aged
- Self Report
- United Kingdom
13 Authors
- 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
- Etienne Vachon-Presseau
1 Application
| Application ID | Title |
|---|---|
| 20802 | A replication study of pain interactions with comorbidities |
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