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Genes, molecules and patients--emerging topics to guide clinical pain research.

ABSTRACT: This review selectively explores some areas of pain research that, until recently, have been poorly understood. We have chosen four topics that relate to clinical pain and we discuss the underlying mechanisms and related pathophysiologies contributing to these pain states. A key issue in pain medicine involves crucial events and mediators that contribute to normal and abnormal pain signaling, but remain unseen without genetic, biomarker or imaging analysis. Here we consider how the altered genetic make-up of familial pains reveals the human importance of channels discovered by preclinical research, followed by the contribution of receptors as stimulus transducers in cold sensing and cold pain. Finally we review recent data on the neuro-immune interactions in chronic pain and the potential targets for treatment in cancer-induced bone pain.

SUBMITTER: Sikandar S

PROVIDER: S-EPMC3793871 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Genes, molecules and patients--emerging topics to guide clinical pain research.

Sikandar Shafaq S Patel Ryan R Patel Sital S Sikander Sanam S Bennett David L H DL Dickenson Anthony H AH

European journal of pharmacology 20130313 1-3

This review selectively explores some areas of pain research that, until recently, have been poorly understood. We have chosen four topics that relate to clinical pain and we discuss the underlying mechanisms and related pathophysiologies contributing to these pain states. A key issue in pain medicine involves crucial events and mediators that contribute to normal and abnormal pain signaling, but remain unseen without genetic, biomarker or imaging analysis. Here we consider how the altered genet ...[more]

PMID: 23500200

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Project description:BackgroundChronic low back pain (cLBP) is a complex with a heterogenous clinical presentation. A better understanding of the factors that contribute to cLBP is needed for accurate diagnosis, optimal treatment, and identification of mechanistic targets for new therapies. The Back Pain Consortium (BACPAC) Research Program provides a unique opportunity in this regard, as it will generate large clinical datasets, including a diverse set of harmonized measurements. The Theoretical Model Working Group was established to guide BACPAC research and to organize new knowledge within a mechanistic framework. This article summarizes the initial work of the Theoretical Model Working Group. It includes a three-stage integration of expert opinion and an umbrella literature review of factors that affect cLBP severity and chronicity.MethodsDuring Stage 1, experts from across BACPAC established a taxonomy for risk and prognostic factors (RPFs) and preliminary graphical depictions. During Stage 2, a separate team conducted a literature review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to establish working definitions, associated data elements, and overall strength of evidence for identified RPFs. These were subsequently integrated with expert opinion during Stage 3.ResultsThe majority (∼80%) of RPFs had little strength-of-evidence confidence, whereas seven factors had substantial confidence for either a positive association with cLBP (pain-related anxiety, serum C-reactive protein, diabetes, and anticipatory/compensatory postural adjustments) or no association with cLBP (serum interleukin 1-beta / interleukin 6, transversus muscle morphology/activity, and quantitative sensory testing).ConclusionThis theoretical perspective will evolve over time as BACPAC investigators link empirical results to theory, challenge current ideas of the biopsychosocial model, and use a systems approach to develop tools and algorithms that disentangle the dynamic interactions among cLBP factors.

Dataset Information

Genes, molecules and patients--emerging topics to guide clinical pain research.

Publications

Genes, molecules and patients--emerging topics to guide clinical pain research.

Similar Datasets

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