DOLORisk
DOLORisk: Understanding risk factors and determinants for neuropathic pain was a project on neuropathic pain, funded by the European Union, between 2015 and 2020. A consortium of European universities and small companies led by the University of Oxford studied the exact nature of risk factors for neuropathic pain and their interaction.
DOLORisk is a precursor to PAINSTORM. In PAINSTORM, we will expand the cohorts recruited in DOLORisk, by recruiting more participants and inviting participants for follow-up visits. This will allow us to study how neuropathic pain evolves over time. We will continue some of the work started in DOLORisk, for instance exploring genetic factors and building risk models for neuropathic pain.
What was DOLORisk about?
Neuropathic pain arises as a consequence of a disease or lesion in the somatosensory nervous system. Neuropathic pain is common, affecting 8% of the population, and will present a rising health burden in the future. It results in significant morbidity, reduces quality of life, and has a major deleterious impact on health in aging. However, not everyone with such a lesion develops significant neuropathic pain, and those who do develop it experience a wide range of severity, impact, and outcomes, and an unpredictable response to evidence-based treatment. This variation in pain prevalence and severity involves a complex interaction between genetics, environmental and clinical factors in a vulnerable individual.
The nature of risk factors for neuropathic pain and their interaction were the focus of DOLORisk. We collected detailed clinical information and questionnaires on large cohorts of participants from which we identified genetic factors whose link with neuropathic pain had not been shown before. We studied the functional impact of ion channel variants and showed that these regulate nociceptor excitability. We found that conditioned pain modulation and electroencephalography were biomarkers that could distinguish between painful and painless neuropathy. However, other specialised tests like threshold tracking and quantitative sensory testing (QST)-derived sensory profile clusters did not show differences between the painful and painless groups. We investigated psychological risk factors and probed the validity of constructs commonly used in pain research. We used longitudinal community based cohorts to build algorithms to predict the development and maintenance of neuropathic pain. These emphasised the importance of psychological risk and resilience factors. These algorithms have been designed to be easily adaptable for clinical use and so can be validated in future independent longitudinal cohorts. We have therefore identified important risk factors for and pathophysiological mechanisms underlying neuropathic pain. The resulting dataset and the unprecedented number of biological samples we hold will be an essential resource for future studies of neuropathic pain.
What did it achieve?
The risk and impact of neuropathic pain represent a complex interplay of environmental and genetic factors which impinge on individual factors such as the emotional and cognitive state. The DOLORisk common protocol was defined to unpick these factors contributing to inter-individual variation. The size of the cohorts and the breadth and depth of our multidisciplinary approach ranging from molecular mechanisms to psychosocial factors have never been realised in the field of NP. With this approach we identified new mutations in sodium channels involved in painful diabetic neuropathy as well as other NP conditions such as non-freezing cold injury and rare conditions like congenital insensitivity to pain. These findings are already having impact as they have been integrated in clinical genetic testing algorithms. The human cellular models which we have validated are now being taken up by the pharmaceutical industry to facilitate analgesic drug development. Our GWAS studies have revealed new genes associated with neuropathic pain which can now be investigated by academics and industry in relation to drug discovery programmes.
In WP6 we probed the content of existing tools to predict chronic pain. In particular, we suggest that the construct of pain catastrophizing is not appropriate to understand the impact of pain on people living with chronic conditions. Instead, we found that pain catastrophizing overlaps largely the construct of pain-related worrying, which is a more neutral description of this psychological dimension of pain. This will aid assessment of the psychological correlates of chronic pain for research and clinical practice.
We developed a risk algorithm using the risk factors which we identified and tested this at a population level. This algorithm could have an important impact on prevention through identifying those at highest risk. A better understanding of the underlying pathophysiological mechanisms and furthermore patient stratification (according to genetic findings and sensory profile) will help improve clinical trial design and help predict which patients will respond to a given treatment. This will lead to more efficient pain management, personalised treatment, and a reduction in the social and economic consequences of living with chronic pain.
