Literature DB >> 24619570

Predicting fibromyalgia, a narrative review: are we better than fools and children?

J N Ablin1, D Buskila.   

Abstract

Fibromyalgia syndrome (FMS) is a common and intriguing condition, manifest by chronic pain and fatigue. Although the pathogenesis of FMS is not yet completely understood, predicting the future development of FMS and chronic pain is a major challenge with great potential advantages, both from an individual as well as an epidemiological standpoint. Current knowledge indicates a genetic underpinning for FMS, and as increasing data are accumulated regarding the genetics involved, the prospect of utilizing these data for prediction becomes ever more attractive. The co-existence of FMS with multiple other functional disorders indicates that the clinical identification of such symptom constellations in a patient can alert the physician to the future development of FMS. Hypermobility syndrome is another clinical (as well as genetic) phenotype that has emerged as a risk factor for the development of FMS. Stressful events, including early life trauma, are also harbingers of the future development of FMS. Functional neuroimaging may help to elucidate the neural processes involved in central sensitization, and may ultimately also evolve into markers of predictive value. Last but not least, obesity and disturbed sleep are clinical (inter-related) features relevant for this spectrum. Future efforts will aim at integrating genetic, clinical and physiological data in the prediction of FMS and chronic pain.
© 2014 European Pain Federation - EFIC®

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Year:  2014        PMID: 24619570     DOI: 10.1002/j.1532-2149.2014.00481.x

Source DB:  PubMed          Journal:  Eur J Pain        ISSN: 1090-3801            Impact factor:   3.931


  3 in total

1.  Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records.

Authors:  Birol Emir; Elizabeth T Masters; Jack Mardekian; Andrew Clair; Max Kuhn; Stuart L Silverman
Journal:  J Pain Res       Date:  2015-06-10       Impact factor: 3.133

2.  Electronic medical record data to identify variables associated with a fibromyalgia diagnosis: importance of health care resource utilization.

Authors:  Elizabeth T Masters; Jack Mardekian; Birol Emir; Andrew Clair; Max Kuhn; Stuart L Silverman
Journal:  J Pain Res       Date:  2015-03-05       Impact factor: 3.133

3.  Impact of daily yoga-based exercise on pain, catastrophizing, and sleep amongst individuals with fibromyalgia.

Authors:  Asimina Lazaridou; Alexandra Koulouris; Jaime K Devine; Monika Haack; Robert N Jamison; Robert R Edwards; Kristin L Schreiber
Journal:  J Pain Res       Date:  2019-10-17       Impact factor: 3.133

  3 in total

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