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A new model to identify and predict chronic pain

A new model to identify and predict chronic pain
Inflammatory, genetic, and functional connectivity markers associated with the risk score for pain. a. Schematic describing the selected biological markers: c- reactive inflammatory protein, a polygenic risk score for the number of pain sites, and a validated brain signature for sustained pain. CRP, C-reactive protein; PRS, polygenic risk score; ToPS, Tonic Pain Signature. b-c. Two-tailed Pearson’s r correlation the association between CRP (log-transformed for parametric estimations) and our risk score in both b. validation set (P < 1.0e-300), and c. discovery set (P < 1.0e-300). d, e. The association between the selected PRS and our risk score in both d. validation set (P < 1.8e-125), and e. discovery set (P < 1.0e-300). f. The association between the ToPS and our risk score in the validation set (P < 2.6e-45). Venn diagram shows the correlation between the biological measures with respect to the three domains and their unions. g. Markers were combined as one variable and examined in the validation set. The respective contribution of biological markers to pain risk score and the number of pain sites are reported in the Venn diagrams. Credit: Nature Medicine (2023). DOI: 10.1038/s41591-023-02430-4

Chronic pain is a complex condition that affects millions of individuals worldwide and understanding its causes and predicting its trajectory remains difficult. However, findings from a recent McGill-led study published in Nature Medicine could improve the understanding and management of chronic pain.

The researchers developed a model that can predict one's likelihood of developing chronic pain as well as the severity and spread of that pain. The study also identified several key risk factors associated with increased chronic pain, including sleeplessness, feelings of distress, fatigue, stressful life events, and a (BMI) above 30.

Using data from the UK Biobank, researchers found that pain tends to spread from one area of the body to another. They developed a model that can predict the number of different pain sites a person may have. With the help of machine-learning, this risk score can distinguish long-lasting pain and predict whether someone will develop widespread chronic pain, how the pain will spread across their body, and whether the pain will be severe.

The implications of this study are far-reaching. "By identifying common biopsychosocial factors associated with , could better personalize treatment plans and improve ," says Dr. Etienne Vachon-Presseau, Assistant Professor in the Faculty of Dental Medicine and Oral Health Sciences and co-author of the study.

More information: Christophe Tanguay-Sabourin et al, A prognostic risk score for development and spread of chronic pain, Nature Medicine (2023). DOI: 10.1038/s41591-023-02430-4

Journal information: Nature Medicine
Provided by McGill University
Citation: A new model to identify and predict chronic pain (2023, July 20) retrieved 15 April 2024 from
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