Unless indicated otherwise, all the publications listed below are freely available in open access.
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Identifying genes involved in neuropathic pain is challenging because pain is subjective and complex. In this study, we built models that identify potential pain-related genes by analyzing multi-omics, genetic information, and protein-protein interaction networks as predictors. By doing this, we can assign a "pain score" to each gene, indicating how likely it is to be involved in pain. We then confirm our findings by comparing them with known genetic variations linked to pain and existing pain-related drug targets. Our findings showed that the most helpful information for predicting pain genes came from understanding the genes' functions, as described by Gene Ontology (GO) pathways. By analyzing the top genes, we identified important biological pathways that could be potential targets for new pain treatments. We also discovered new genes not previously linked to pain. This work offers new insights into pain research and could also be applied to other diseases like neurodegenerative disorders, psychiatric conditions, and cancer.
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Extreme pain conditions, which are often caused by rare genetic mutations, offer insights into the fundamental mechanisms of pain. In this study, participants with a history of lifestyle-altering sensory disorder underwent whole genome sequencing (WGS). WGS describes the sequencing of an individual’s entire genome, and is the most comprehensive form of genomic testing currently in clinical use. Clinically relevant variants were found in over 10% of the participants, the majority of which were located in ion channels, highlighting the role of ion channel variants in extreme neuropathic pain disorders.
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Themistocleous AC, Baskozos G, Blesneac I, Comini M, Megy K, Chong S, Deevi SVV, Ginsberg L, Gosal D, Hadden RDM, Horvath R, Mahdi-Rogers M, Manzur A, Mapeta R, Marshall A, Matthews E, McCarthy MI, Reilly MM, Renton T, Rice ASC, Vale TA, van Zuydam N, Walker SM, Woods CG, Bennett DLH. Investigating genotype-phenotype relationship of extreme neuropathic pain disorders in a UK national cohort. Brain Commun. 2023 Feb 20;5(2):fcad037.
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Links: Original publication | PMID 36895957
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Related research themes: Genetic factors (WP5), DOLORisk
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Big Data and machine learning techniques offer opportunities to investigate the effects of psychological factors on pain outcomes, but only if the data is of high quality and the underpinning causal assumptions are considered. We argue that there is room for improvement and identify some challenges in the evidence base concerning the effect of psychological factors on the development and maintenance of chronic pain. In particular, there is a need to be more transparent about causal assumptions in research. This will lead to better research designs, more appropriate statistical analyses, and constructive discussions and productive tensions that improve our science.
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Establishing cause-and-effect relationships is a difficult question in research. In this commentary, we encourage researchers to engage in causal thinking by using Directed Acyclic Graphs, which rely on expert knowledge rather than experimental data to reveal and display relationships between variables and outcomes.
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Studies of neuropathic pain have been hindered by issues such as a lack of consensus on phenotyping and small sample sizes. The key to overcoming these issues is through the creation of large consortia and biorepositories, where a common approach can be taken to chronic pain phenotyping. This review describes the approach that was used for studying neuropathic pain in DOLORisk and how this has informed current projects such as PAINSTORM, the rephenotyping of UK Biobank, and other endeavours.
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Hebert HL, Pascal MMV, Smith, BH, Wynick, D, Bennett, DLH. Big data, big consortia, and pain: UK Biobank, PAINSTORM, and DOLORisk. Pain Rep 2023; 8(5):p e1086.
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Links: Original publication | PMID 38225956
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Related research themes: Protocols and recruitment (WP1), Genetic factors (WP5)
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Dorsal root ganglia (DRG) neurons have been well described for their role in driving both acute and chronic pain. Although nerve injury is known to cause transcriptional dysregulation, how this differs across neuronal subtypes and the impact of sex is unclear. Here, we study the deep transcriptional profiles of multiple murine DRG populations in early and late pain states while considering sex.
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Previous epidemiological studies of neuropathic pain have reported a range of prevalences and factors associated with the disorder. This study aimed to verify these characteristics in a large UK cohort.
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Baskozos G, Hebert HL, Pascal MMV, Themistocleous AC, Macfarlane GJ, Wynick D, Bennett DLH, Smith BH. Epidemiology of neuropathic pain: an analysis of prevalence and associated factors in UK Biobank. Pain Rep 2023; 8(2): e1066.
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Links: Original publication | PMID 37090682
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Related research themes: Genetic factors (WP5)
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This review aims to discuss the planning and preparation for completing a meta-analysis, review commonly used meta-analysis models, and evaluate the clinical implications of meta-analysis in pain research.
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Using data from the DOLORisk cohorts, we built models that classify painful versus painless diabetic peripheral neuropathy using quality of life, lifestyle, demographics, personality and psychology traits, and biochemical and clinical variables as predictors.
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Baskozos G, Themistocleous AC, Hebert HL, Pascal MMV, John J, Callaghan BC, Laycock H, Granovsky Y, Crombez G, Yarnitsky D, Rice ASC, Smith BH, Bennett DLH. Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts. BMC Med Inform Decis Mak 2022; 22(1): 144.
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Links: Original publication | PMID 35644620
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Related research themes: Genetic factors (WP5), DOLORisk
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This paper reviews the current state-of-the-art of peripheral nerve magnetic resonance imaging (MRI) in diabetic and HIV symmetrical polyneuropathy.
