TY  - JOUR
A1  - Lötsch, Jörn
A1  - Geisslinger, Gerd
A1  - Heinemann, Sarah
A1  - Lerch, Florian
A1  - Oertel, Bruno Georg
A1  - Ultsch, Alfred
T1  - Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B-induced local skin hypersensitization in healthy subjects : a machine-learned analysis
T2  - Pain
N2  - 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.
KW  - Quantitative sensory testing
KW  - Human experimental pain models
KW  - Heat pain
KW  - Cold pain
KW  - Pressure pain
KW  - Sex differences
KW  - Subgroup identification
KW  - Machine-learning
KW  - Data science
KW  - Neuronal networks
Y1  - 2017
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/45611
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-456113
SN  - 1872-6623
SN  - 0304-3959
N1  - 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.
VL  - 159
IS  - 1
SP  - 11
EP  - 24
PB  - Lippincott Williams and Wilkins
CY  - New York, NY [u. a.]
ER  -