Periodic Reporting for period 3 - PainFACT (Molecular Mechanisms Associating Chronic Pain with Fatigue, Affective Disorders, Cardiovascular Disease and Total Comorbidity)
Berichtszeitraum: 2023-01-01 bis 2024-06-30
Chronic pain is a common health problem with major impact on quality of life and functioning. It is the leading cause of disability and societal costs due to productivity loss are huge, - far greater than those of cardiovascular disease, diabetes, or cancer. The causes of chronic pain are poorly understood, symptomatic treatment efficacy is poor, and curative treatment is lacking for most chronic pain condition. The sheer number of patients affected also presents a challenge and there is a need to find screening methods for early identification of patients at risk for developing severe disabling chronic pain. Notably, chronic pain patients also tend to suffer from additional conditions, including anxiety, depression, fatigue, cardiovascular diseases and premature mortality. The full extent of this clustering of diseases with chronic pain is unknown, and the mechanisms causing these conditions to cluster together remain to be identified.
The overall objective of PainFACT is therefore to identify patterns of disease that co-occur with chronic pain and to identify the mechanisms that cause these diseases to occur together. Through analysis of patient data from the population of an entire country, PainFACT aims to characterize clusters of medical conditions that are associated with chronic pain. Using state-of-the-art, genetic techniques, measurements of proteins in blood, and brain imaging analysis from humans and mice, PainFACT will identify mechanisms and develop predictive models for chronic pain and related conditions.
The overall objective of PainFACT is therefore to identify patterns of disease that co-occur with chronic pain and to identify the mechanisms that cause these diseases to occur together. Through analysis of patient data from the population of an entire country, PainFACT aims to characterize clusters of medical conditions that are associated with chronic pain. Using state-of-the-art, genetic techniques, measurements of proteins in blood, and brain imaging analysis from humans and mice, PainFACT will identify mechanisms and develop predictive models for chronic pain and related conditions.
PainFACT is executed in overlapping discovery and validation phases with analysis of human and animal data. Key to the discovery phase is the analysis of very large datasets. In humans this encompasses analysis of the clustering of diseases using health registry data for the entire Norwegian population with more than 5M individuals, genetic analysis of large population-based studies, such as UK-Biobank (500k) and Danish Bloodbank (500k), and analysis of blood proteins, brain imaging, as well as more refined studies of relationships between clinical outcomes using data from the Rotterdam Study and Tromsø Study where extensive clinical examinations are available. In animal models, a large discovery sample of nearly 200 outbred mice were exposed to either chronic pain or sham treatment with measurement of pain sensitivity, anxiety, depression, memory, metabolic markers, atherosclerosis, brain imaging and gene expression in the brain and spinal cord.
In total the project has published 9 papers, with additional papers submitted or in press. Among these, was a study of the genetics of back-pain (Bjornsdottir, et al. 2022, Nature Communications), identifying 41 genetic variants. Aside from migraine, the genetics of pain has lagged behind other fields, and getting a grip on the genetics of well-defined common pain outcomes is of central importance for understanding the molecular mechanisms of pain.
Most findings in the study thus far are preliminary and cannot be shared publicly before they have undergone peer review and publication. At a general level these findings suggest that the project is approaching a tipping point where synergistic effects between methods are emerging. For instance, human and animal arms of PainFACT have produced similar results with respect to the relationship between cardiovascular disease and pain, indicating that further elucidation of biological mechanisms in animal models is feasible and relevant.
In total the project has published 9 papers, with additional papers submitted or in press. Among these, was a study of the genetics of back-pain (Bjornsdottir, et al. 2022, Nature Communications), identifying 41 genetic variants. Aside from migraine, the genetics of pain has lagged behind other fields, and getting a grip on the genetics of well-defined common pain outcomes is of central importance for understanding the molecular mechanisms of pain.
Most findings in the study thus far are preliminary and cannot be shared publicly before they have undergone peer review and publication. At a general level these findings suggest that the project is approaching a tipping point where synergistic effects between methods are emerging. For instance, human and animal arms of PainFACT have produced similar results with respect to the relationship between cardiovascular disease and pain, indicating that further elucidation of biological mechanisms in animal models is feasible and relevant.
Given the limited knowledge of the causes of chronic pain and pain comorbidity, it is expected that PainFACT will yield several novel results with long-term impact on the field.
1. PainFACT will identify patterns of diseases that co-occur with pain conditions. PainFACT will furthermore develop predictive models and screening methods for identifying patients at risk of developing several diseases (multimorbidity), of importance for prioritizing patients and tailoring treatment to the individual patient (precision medicine).
2. PainFACT will identify biological mechanisms that are associated with pain, yielding insight into the root causes of chronic pain. This is expected to yield novel targets for pharmacological research, ultimately paving the way for the development of new analgesic treatments.
3. PainFACT will identify biological mechanisms that are associated with clusters of diseases that co-occur with pain, yielding insight into why pain patients so often suffer from e.g. mental health problems, cardiovascular disease, and other conditions. This will open a window of opportunity for developing treatments targeting patterns of disease, rather than individual diseases. With an ageing population and an increasing number of individuals suffering from many diseases there is a pressing need to move beyond the one-disease-one-drug approach to treatment to avoid the frequent side-effects that occur when patients take many medicines together.
4. PainFACT will identify characteristics of the brain that are associated with pain and pain comorbidity. This is a key to understanding how pain impacts the central nervous system (i.e. cognition and emotion) and can potentially be developed as a method for monitoring the impact of pain on diseases of the central nervous system, such as anxiety, depression and fatigue.
The focus of PainFACT is on mechanisms and basic science, and as such the project is not directed at creating change at a societal level. However, as chronic pain is the leading cause of disability and arguably the single most expensive health-care problem, it is to be expected that PainFACT will have major indirect positive impact at the societal level. For instance: The ability to predict which patients will develop severe pain and multiple co-occurring health problems would be of major value in prioritizing patients for more extensive treatment in health-care systems that are pressed for resources. Should PainFACT eventually lead to development of improved pain treatments, this would be of immense value for reducing the suffering and societal costs associated with pain. Though such outcomes lie outside the scope of the project itself, PainFACT was nevertheless designed to provide the foundation for further exploitation by e.g. the pharmacological industry and will engage in close dialogue to ensure the further exploitation of project results.
1. PainFACT will identify patterns of diseases that co-occur with pain conditions. PainFACT will furthermore develop predictive models and screening methods for identifying patients at risk of developing several diseases (multimorbidity), of importance for prioritizing patients and tailoring treatment to the individual patient (precision medicine).
2. PainFACT will identify biological mechanisms that are associated with pain, yielding insight into the root causes of chronic pain. This is expected to yield novel targets for pharmacological research, ultimately paving the way for the development of new analgesic treatments.
3. PainFACT will identify biological mechanisms that are associated with clusters of diseases that co-occur with pain, yielding insight into why pain patients so often suffer from e.g. mental health problems, cardiovascular disease, and other conditions. This will open a window of opportunity for developing treatments targeting patterns of disease, rather than individual diseases. With an ageing population and an increasing number of individuals suffering from many diseases there is a pressing need to move beyond the one-disease-one-drug approach to treatment to avoid the frequent side-effects that occur when patients take many medicines together.
4. PainFACT will identify characteristics of the brain that are associated with pain and pain comorbidity. This is a key to understanding how pain impacts the central nervous system (i.e. cognition and emotion) and can potentially be developed as a method for monitoring the impact of pain on diseases of the central nervous system, such as anxiety, depression and fatigue.
The focus of PainFACT is on mechanisms and basic science, and as such the project is not directed at creating change at a societal level. However, as chronic pain is the leading cause of disability and arguably the single most expensive health-care problem, it is to be expected that PainFACT will have major indirect positive impact at the societal level. For instance: The ability to predict which patients will develop severe pain and multiple co-occurring health problems would be of major value in prioritizing patients for more extensive treatment in health-care systems that are pressed for resources. Should PainFACT eventually lead to development of improved pain treatments, this would be of immense value for reducing the suffering and societal costs associated with pain. Though such outcomes lie outside the scope of the project itself, PainFACT was nevertheless designed to provide the foundation for further exploitation by e.g. the pharmacological industry and will engage in close dialogue to ensure the further exploitation of project results.