Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B-induced local skin hypersensitization in healthy subjects : a machine-learned analysis

The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we asses
The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we assessed established experimental models of heat hyperalgesia of the skin, consisting of local ultraviolet-B (UV-B) irradiation or capsaicin application, in 82 healthy subjects using a variety of noxious stimuli. We extended the original heat stimulation by applying cold and mechanical stimuli and assessing the hypersensitization effects with a clinically established quantitative sensory testing (QST) battery (German Research Network on Neuropathic Pain). This study provided a 246 × 10-sized data matrix (82 subjects assessed at baseline, following UV-B application, and following capsaicin application) with respect to 10 QST parameters, which we analyzed using machine-learning techniques. We observed statistically significant effects of the hypersensitization treatments in 9 different QST parameters. Supervised machine-learned analysis implemented as random forests followed by ABC analysis pointed to heat pain thresholds as the most relevantly affected QST parameter. However, decision tree analysis indicated that UV-B additionally modulated sensitivity to cold. Unsupervised machine-learning techniques, implemented as emergent self-organizing maps, hinted at subgroups responding to topical application of capsaicin. The distinction among subgroups was based on sensitivity to pressure pain, which could be attributed to sex differences, with women being more sensitive than men. Thus, while UV-B and capsaicin share a major component of heat pain sensitization, they differ in their effects on QST parameter patterns in healthy subjects, suggesting a lack of redundancy between these models.
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Metadaten
Author:Jörn Lötsch, Gerd Geisslinger, Sarah Heinemann, Florian Lerch, Bruno Georg Oertel, Alfred Ultsch
URN:urn:nbn:de:hebis:30:3-456113
DOI:http://dx.doi.org/10.1097/j.pain.0000000000001008
ISSN:1872-6623
ISSN:0304-3959
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=28700537
Parent Title (English):Pain
Publisher:Lippincott Williams and Wilkins
Place of publication:New York, NY [u. a.]
Document Type:Article
Language:English
Year of Completion:2017
Date of first Publication:2017/08/16
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/02/01
Tag:Cold pain; Data science; Heat pain; Human experimental pain models; Machine-learning; Neuronal networks; Pressure pain; Quantitative sensory testing; Sex differences; Subgroup identification
Volume:159
Issue:1
Pagenumber:14
First Page:11
Last Page:24
Note:
Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association for the Study of Pain. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non CommercialNo Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
HeBIS PPN:428736858
Institutes:Medizin
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell - Keine Bearbeitung 4.0

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