Biological markers and psychosocial factors predict chronic pain conditions.
Authors
Affiliations (13)
Affiliations (13)
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, Quebec, Canada. [email protected].
- Department of Anesthesia, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada. [email protected].
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, Quebec, Canada.
- Department of Anesthesia, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
- Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada.
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada.
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
- Center for Translational Pain Medicine, Duke University, Durham, NC, USA.
- Alan Edwards Pain Management Unit, McGill University Health Center, Montreal, Quebec, Canada.
- Department of Psychology, McGill University, Montreal, Quebec, Canada.
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, Quebec, Canada. [email protected].
- Department of Anesthesia, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada. [email protected].
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada. [email protected].
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.