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 requir...
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Elsevier
2025-02-01
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| Series: | Pharmacological Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1043661825000052 |
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| author | Daniel Segelcke Julia R. Sondermann Christin Kappert Bruno Pradier Dennis Görlich Manfred Fobker Jan Vollert Peter K. Zahn Manuela Schmidt Esther M. Pogatzki-Zahn |
| author_facet | Daniel Segelcke Julia R. Sondermann Christin Kappert Bruno Pradier Dennis Görlich Manfred Fobker Jan Vollert Peter K. Zahn Manuela Schmidt Esther M. Pogatzki-Zahn |
| author_sort | Daniel Segelcke |
| collection | DOAJ |
| description | 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 and after an experimental incision injury. We determined multi-modal features ranging from pain characteristics and psychological questionnaires to blood plasma proteomics. Outcome measures included 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 features. A combinatorial set of distinct features 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 of 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 of stratifying patients and identifying potential new targets, as well as mechanistic correlates, for postsurgical pain. |
| format | Article |
| id | doaj-art-7b090af980e447dfa1bbc9b9336df11e |
| institution | Kabale University |
| issn | 1096-1186 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Pharmacological Research |
| spelling | doaj-art-7b090af980e447dfa1bbc9b9336df11e2025-02-08T04:59:40ZengElsevierPharmacological Research1096-11862025-02-01212107580Blood proteomics and multimodal risk profiling of human volunteers after incision injury: A translational study for advancing personalized pain management after surgeryDaniel Segelcke0Julia R. Sondermann1Christin Kappert2Bruno Pradier3Dennis Görlich4Manfred Fobker5Jan Vollert6Peter K. Zahn7Manuela Schmidt8Esther M. Pogatzki-Zahn9Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster 44651, GermanyDepartment of Pharmaceutical Sciences, Division of Pharmacology and Toxicology, Systems Biology of Pain Group, University of Vienna, UZA II, Josef-Holaubek-Platz 2, Vienna A-1090, AustriaMax-Planck Institute for Multidisciplinary Sciences, City Campus, Hermann-Rein-Straße 3, Göttingen 37075, GermanyDepartment of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, GermanyInstitute of Biostatistics and Clinical Research, University of Münster, Albert-Schweitzer-Campus 1, Münster 44651, GermanyCentre of Laboratory Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster 44651, GermanyDepartment of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster 44651, Germany; Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UKDepartment of Anesthesiology, Intensive Care and Pain Medicine, BG University Hospital Bergmannsheil, Ruhr-Universität Bochum, Bürkle de la Camp-Platz 1, Bochum 44789, GermanyDepartment of Pharmaceutical Sciences, Division of Pharmacology and Toxicology, Systems Biology of Pain Group, University of Vienna, UZA II, Josef-Holaubek-Platz 2, Vienna A-1090, Austria; Corresponding author at: Department of Pharmaceutical Sciences, Division of Pharmacology and Toxicology, Systems Biology of Pain Group, University of Vienna, UZA II, Josef-Holaubek-Platz 2, Vienna A-1090, Austria.Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster 44651, Germany; Correspondence to: Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, A1, Muenster 48149, Germany.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 and after an experimental incision injury. We determined multi-modal features ranging from pain characteristics and psychological questionnaires to blood plasma proteomics. Outcome measures included 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 features. A combinatorial set of distinct features 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 of 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 of stratifying patients and identifying potential new targets, as well as mechanistic correlates, for postsurgical pain.http://www.sciencedirect.com/science/article/pii/S1043661825000052Blood plasma proteomicsPain phenotypesChronic postsurgical painTranslational pain researchDrug repositioningPersonalized pain management |
| spellingShingle | Daniel Segelcke Julia R. Sondermann Christin Kappert Bruno Pradier Dennis Görlich Manfred Fobker Jan Vollert Peter K. Zahn Manuela Schmidt Esther M. Pogatzki-Zahn Blood proteomics and multimodal risk profiling of human volunteers after incision injury: A translational study for advancing personalized pain management after surgery Pharmacological Research Blood plasma proteomics Pain phenotypes Chronic postsurgical pain Translational pain research Drug repositioning Personalized pain management |
| title | Blood proteomics and multimodal risk profiling of human volunteers after incision injury: A translational study for advancing personalized pain management after surgery |
| title_full | Blood proteomics and multimodal risk profiling of human volunteers after incision injury: A translational study for advancing personalized pain management after surgery |
| title_fullStr | Blood proteomics and multimodal risk profiling of human volunteers after incision injury: A translational study for advancing personalized pain management after surgery |
| title_full_unstemmed | Blood proteomics and multimodal risk profiling of human volunteers after incision injury: A translational study for advancing personalized pain management after surgery |
| title_short | Blood proteomics and multimodal risk profiling of human volunteers after incision injury: A translational study for advancing personalized pain management after surgery |
| title_sort | blood proteomics and multimodal risk profiling of human volunteers after incision injury a translational study for advancing personalized pain management after surgery |
| topic | Blood plasma proteomics Pain phenotypes Chronic postsurgical pain Translational pain research Drug repositioning Personalized pain management |
| url | http://www.sciencedirect.com/science/article/pii/S1043661825000052 |
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