BLOOD PROTEOMICS AND MULTIMODAL RISK PROFILING of human volunteers after incision injury: a translational study for advancing Personalized Pain Management After Surgery

A significant number of patients develop chronic pain after surgery, but prediction of those who are at risk is currently not possible. Thus, prognostic prediction models that include bio-psycho, social and physiological factors in line with the complex nature of chronic pain would be urgently required. Here, we performed a translational study in male volunteers before an experimental incision injury. We determined multi-modal factors ranging from pain characteristics, psychological questionnaires to blood proteomics. Outcome measures after incision were pain intensity ratings and the extent of the area of hyperalgesia to mechanical stimuli surrounding the incision as a proxy of central sensitization. A multi-step logistic regression analysis was performed to predict outcome measures based on feature combinations using data-driven cross-validation and prognostic model development. Phenotype-based stratification resulted in the identification of low and high responders for both outcome measures. Regression analysis revealed prognostic proteomic, specific psychophysical and psychological parameters. A combinatorial set of distinct parameters enabled us to predict outcome measures with increased accuracy compared to using single features. Remarkably, in high responders, protein network analysis suggested a protein signature characteristic for low-grade inflammation. Alongside, in silico drug repurposing highlighted potential treatment options employing antidiabetic and anti-inflammatory drugs. Taken together, we present here an integrated pipeline that harnesses bio-psycho-physiological data for prognostic prediction in a translational approach. This pipeline opens new avenues for clinical application with the goal tostratify patients and identify potential new targets as well as mechanistic correlates for postsurgical pain. GERMAN CLINICAL TRIALS REGISTRY: (DRKS-ID: DRKS00016641).