Blood Proteomics and Pain - A Translational Study to Prognosticate Pain Phenotypes and Assess New Biomarkers for Preventing Pain in Humans

Abstract

Personalized strategies in pain management and prevention should be based on individual risk factors as early as possible, but the factors most relevant are not yet known. An innovative approach would be to integrate multi-modal risk factors, including blood proteomics, in predicting high pain responders and using them as targets for personalized treatment options. Here, we determined and mapped multi-modal factors to prognosticate a phenotype with high risk of developing pain and hyperalgesia after an experimental incision in humans. We profiled unbiased blood plasma proteome signature of 26 male volunteers, assessed psychophysical and psychological aspects before incision injury. Outcome measures were pain intensity ratings and the extent of the area of hyperalgesia to mechanical stimuli surrounding the incision as a proxy for central sensitization. Phenotype-based stratification resulted in the identification of low- and high-responders for the two different outcome measures. Logistic regression analysis revealed prognostic potential for blood plasma proteins and for psychophysical and psychological parameters. The combination of certain parameters increased the prognostic accuracy for both outcome measures, exceeding 97%. In high-responders, term-term-interaction network analysis showed a proteome signature of a low-grade inflammation reaction. Intriguingly,in silicodrug repurposing indicates a high potential for specific antidiabetic and anti-inflammatory drugs already available. In conclusion, we show an integrated pipeline that provides a valuable resource for patient stratification and the identification of (i) multi-feature prognostic models, (ii) treatment targets, and (iii) mechanistic correlates that may be relevant for individualized management of pain and its long-term consequences.One Sentence SummaryUnbiased identification of blood protein signatures in a translational human postoperative pain model provides new targets for managing pain.