A new study published in JAMA Neurology reports two biomarkers that can reliably predict pain sensitivity in individuals and even pain severity.
Pain causes changes in the brain, with some regions being hyperexcited. Thus, if these signals can be measured in a non-invasive and reliable way, then one can measure pain sensitivity. This can help understand pain severity, too, and thus provide proper treatment.
One of the issues with pain is that it is challenging to measure. Hence, doctors mostly depend on subjective measures, that is, how patients define their pain. However, this method is highly unreliable since some may describe even relatively moderate pain as severe while others may tolerate pain better.
Pain is the most common sign or symptom reported in clinics, and yet there is no reliable way to measure pain. Pain is so common that it is estimated that at any given time, about 1.7 billion adults are living with musculoskeletal pains alone. But, of course, there are many other pain types, too. Pain causes much distress, and poorly managed acute pain becomes chronic, causing much harm to health.
Researchers say that they have identified a way of measuring pain sensitivity for the first time. Not only that, but it may also help predict the risk of chronic pain and reduce its burden through timely treatment.
For this study, researchers enrolled 150 patients living with jaw pain caused by temporomandibular disorders. There were relatively young patients aged 18 to 44 years. They used a non-invasive method or electroencephalography (EEG) recording to measure the pain sensitivity. EEG involves placing electrodes on the head and recording certain brain wave patterns. Improvements in computing, recording methods, equipment, and statistical methods have enabled the identification of even subtle changes in EEG patterns. In this study, researchers focused on two measures: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME).
Researchers found that slow PAF followed by reduced CME was an excellent predictor of prolonged pain episodes or chronic pain. Such individuals were more likely to experience higher pain lasting for weeks or more.
Further, they also found that low levels of CME in lower back patients indicated a much greater risk of chronic pain. Such patients were more likely to develop low back pain lasting more than six months.
This is the first of its kind of study that demonstrated that measuring PAF and CME may help predict pain severity and the risk of developing chronic pain. Researchers are calling these findings a major breakthrough in pain medicine.
Moreover, it is worth noticing that these biomarkers had 88% accuracy, which is quite high. Early detection of pain sensitivity and the risk of developing chronic pain may help introduce early interventions, thus reducing the risk of chronic pain.
Considering the high accuracy of these biomarkers, researchers are now validating their use in real-world conditions. Suppose they demonstrate similar degree of reliability in real-world conditions. In that case, clinicians may start using them in the near future to understand pain and predict the risk of chronic health conditions.
Source:
Chowdhury, N. S., Bi, C., Furman, A. J., Chiang, A. K. I., Skippen, P., Si, E., Millard, S. K., Margerison, S. M., Spies, D., Keaser, M. L., Da Silva, J. T., Chen, S., Schabrun, S. M., & Seminowicz, D. A. (2025). Predicting Individual Pain Sensitivity Using a Novel Cortical Biomarker Signature. JAMA Neurology. https://doi.org/10.1001/jamaneurol.2024.4857
Dr. Gurpreet Singh Padda, MD, MBA, MHP
