Biological markers and psychosocial factors predict chronic pain conditions
Bibliographic Data
| ID | 4615140 |
|---|---|
| Authors | Matt Fillingim (0000-0002-4774-1565, McGill University Health Centre, corresponding author), Christophe Tanguay-Sabourin (0000-0003-0426-8322, McGill University), Marc Parisien (0000-0003-2924-5960, McGill University), Azin Zare (0000-0002-7476-9897, McGill University), Gianluca V Guglietti (0000-0003-3488-1558, McGill University), Jax Norman (0000-0002-3657-356X, McGill University), Bogdan Petre (0000-0002-8437-168X, Dartmouth College), Andrey Bortsov, Andrey V Bortsov (0000-0002-6838-5854, Duke University), Mark Ware (0000-0003-2859-7411), Mark A Ware (0000-0002-1286-1719, McGill University Health Centre), Jordi Pérez (0000-0003-2303-1339, McGill University Health Centre), Mathieu Roy (0000-0001-6405-6765, McGill University), Luda Diatchenko (0000-0002-1350-6727, McGill University), Etienne Vachon-Presseau (0000-0002-8681-3154, McGill University Health Centre, corresponding author) |
| Year | 2025 |
| Volume | 9 |
| Issue | 8 |
| Pages | 1710-1725 |
| Publication date | 2025-05-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Nature Human Behaviour (JOURNAL) |
| Journal identifiers | ISSN: 2397-3374 • E-ISSN: 2397-3374 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1038/s41562-025-02156-y |
| PMID | 40355673 |
| OpenAlex | W4410309278 |
| Language | EN |
| References cited | 48 |
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
Biobank · Bioinformatics · Chronic pain · Physical therapy · Psychiatry · Psychosocial · Rheumatoid arthritis · Fibromyalgia and Chronic Fatigue Syndrome Research · Infrared Thermography in Medicine · Medicine · Musculoskeletal pain and rehabilitation · Internal Medicine
| Citation velocity | historical |
|---|---|
| Highly cited | No |