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DOLORisk: Understanding risk factors and determinants for neuropathic pain

Periodic Reporting for period 4 - DOLORisk (DOLORisk: Understanding risk factors and determinants for neuropathic pain)

Periodo di rendicontazione: 2019-07-01 al 2020-09-30

Neuropathic pain (NP) arises as a consequence of a disease or lesion in the sensory nervous system. NP affects 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 develops significant NP following injury to the nervous system, and those who do develop it include a wide range of severity, impact and outcomes, and an unpredictable response to treatment. This variation involves a complex interaction between genetics, environmental and clinical factors in a vulnerable individual.
The nature of risk factors for NP 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 NP had not been shown before. We studied the functional impact of selected 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 NP. 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 NP. The resulting dataset and the unprecedented number of biological samples we hold will be an essential resource for future studies of NP.
1. Harmonisation
We started by harmonising the study protocol across recruiting sites and defining a core protocol consisting of self-reported questionnaires for postal surveys, and an extended protocol for in-depth data collection with clinic visits. The 8 DOLORisk sites recruited over 2,000 participants between 2015 and 2019 following the extended protocol, and over 9,000 participants in population cohorts following the core protocol. We also used it as a basis to explore possible collaborations, from which we gained 2,000 samples with data comparable to DOLORisk. The DOLORisk method was applied to UK Biobank to collect more precise data on chronic pain, which will give new insights into the genetics of NP.
2. Genetics
We put together the largest cohort of people living with NP to date, adequately powered for genome-wide association studies. We identified association between NP and the loci encoding the genes KCNT2, LHX8 and SLC25A3.
We also identified variants in ion channel genes in participants with diabetic neuropathy (eg SCN9A encoding the voltage gated sodium channel NaV1.7) and some rare neuropathic disorders. We studied the functional impact of these variants and their links with phenotypes. We also found changes in gene expression in the skin which relate to painful (vs painless) diabetic neuropathy.
3. Central and peripheral physiological processes
Threshold tracking and QST clusters were unable to distinguish between participants with painful neuropathy and those with painless neuropathy. These techniques might be better suited to compare neuropathic and non-neuropathic states, or could be further investigated to track the evolution of nerve function in patients with neuropathy in a longitudinal study design.
Conditioned pain modulation tests showed that participants with painful diabetic neuropathy had more efficient pain inhibition mechanisms following a painful stimulus than the painless diabetic neuropathy group. EEG showed different connectivity patterns in the brain between the painful and painless groups.
4. Risk algorithms
Based on the psychological data collected in the common protocol, we built a risk algorithm for the development, maintenance and worsening of NP. The best fitting model is that designed for predicting the development of NP after 18 months, using quality of life, sleep disturbance, openness to new experiences, adverse childhood experiences and smoking as predictors. The main genetic component discovered in the population cohorts did not significantly improve the models’ predictive ability.
5. Dissemination
All DOLORisk peer-reviewed publications are available in open access. We shared our progress at national and international conferences, uploaded our results to public databases, and established links with many projects across Europe. Our experience in data harmonisation has been a useful lesson for other projects as well as grant providers, and DOLORisk has been cited many times as an example of how large international collaborations can advance research in their fields.
The risk and impact of NP represents 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 (Blesneac 2018) as well as other NP conditions such as non-freezing cold injury and rare conditions like congenital insensitivity to pain (McDermott 2019). 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 associations of NP with SCN25A, KCNT2 and LHX8 genes and these 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 have 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 NP 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.
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