Project description
Identifying molecular mechanisms of pain related disorders
Chronic pain patients tend to suffer from numerous comorbid conditions including anxiety, depression, fatigue, cardiovascular diseases and premature mortality. The full extent of this clustering of diseases, and the mechanisms that cause it, remains to be identified. By analysing patient data of an entire country, EU-funded PainFACT aims to characterise clusters of medical conditions associated with chronic pain. Using state-of-the-art genomic, proteomic, and brain imaging data from humans and mice, PainFACT will identify molecular mechanisms and develop predictive algorithms for new-onset chronic pain and pain related comorbidity. Project results are expected to have major impact on the diagnostic classification of pain, early identification of patients at risk of multimorbidity, and the identification of treatment targets for development of new medicines.
Objective
Chronic pain (CP) is the leading cause of disability, and is strongly associated with fatigue, anxiety and depression ─ also major contributors to disability, and with cardiovascular disease (CVD) and mortality. Twin studies indicate that these associations are a consequence of common causal mechanisms. The main objective of PainFACT is to identify these mechanisms. Using hypothesis-free genomic, proteomic, transcriptomic and brain-imaging discovery in available human studies and in a large cohort of outbred mice with multiple comorbidities, we aim to identify biomarkers that are associated across conditions. Predictive algorithms will be developed through machine learning techniques and tested in prospective analysis. Mendelian randomization approaches will be applied to test for causality. Mechanistic studies will be carried out in validated behavioral and atherosclerotic mouse models. Predictive markers will be tested as possible mediators of effects of lifestyle and obesity. Unique features of this program of research is the strong emphasis on experimental pain models and brain imaging techniques, facilitating translation of findings between mice and humans, and exploitation of the largest study of experimental pain worldwide and of multiple clinical datasets ranging in size from tens of thousands to 1.1 million. A custom protein panel will be developed together with sex and age stratified algorithms, with expected impact for the prediction and monitoring of disease and comorbidity, and for tracking effects of life-style changes. It is also expected that PainFACT results will have major impact on the diagnostic criteria and classification of affective disorders and CP. The identification of novel causal biomarkers will provide new targets for development of medicines and yield new insight into the causes of comorbidity.
Fields of science
- social sciencessociologydemographymortality
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- medical and health sciencesclinical medicinecardiologycardiovascular diseases
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- medical and health scienceshealth sciencesnutritionobesity
Keywords
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
0456 Oslo
Norway