HCR.20.047 – Chronic postsurgical pain genetics consortium

Route: Health care research, sickness prevention and treatment

Cluster question: 081 How will our knowledge of genetics play a role in analysing, screening for, and treating diseases?

More than 300 million surgeries are performed worldwide each year. Chronic postsurgical pain (CPSP) is a debilitating and costly health problem affecting on average 20% of the patients undergoing surgery. CPSP has a large effect on the quality of life of the patients and their relatives with patients reporting higher incidences of sleep disturbances, depressive symptoms and comorbid disorders. Due to the pain and the comorbidities, some patients enter a vicious cycle wherein the different problems including chronic pain further seem to worsen their health status and quality of life (QoL), leading to high costs for society (e.g. health care costs, unemployment). Preventive interventions are necessary to avert postoperative pain to become chronic. However, the influence of preoperative status on long-term postoperative outcome needs to be defined more clearly. To identify preoperative predictors is important, both for an early recognition of patients at risk and for addressing and choosing the most optimal treatment in relation to this risk. Thus far, some risk factors have been proposed, but preoperative objective markers such as genetic factors are substantially lacking.
To study the impact and predictive power of genetic risk factors in CPSP a genome-wide association study is necessary. This data will be incorporated in a prediction model based on the currently known risk factors. Furthermore, genome-wide significant risk factors will be studied in a preclinical study to elucidate the underlying biological mechanisms and test possible interventions. The data will be translated to the clinic into preventive or therapeutic perioperative interventions for the patients at risk. Lastly, we will conclude the study with a feasibility/implementation study for integration in the clinical practice. The overall goal is to optimize the prediction model for CPSP by integrating genetic risk factors, profile the biological mechanism underlying CPSP and identify treatment targets in a translational approach.

Keywords

genomic prediction model, GWAS, Pain, Personalized medicine, pharmacological intervention

Other organisations

Maastricht University, Radboud Medical Center (RUMC)

Submitter

Organisation Maastricht University Medical Center (UM)
Name Prof. Dr. Wolfgang Buhre
E-mail wolfgang.buhre@mumc.nl
Website www.mumc.nl/specialisten/buhre