J Lötsch1,2, V Dimova1,3, A Ultsch4, I Lieb1, M Zimmermann4, G Geisslinger1,2, B G Oertel1,2. 1. Institute of Clinical Pharmacology, Goethe - University, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany. 2. Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Project Group Translational Medicine and Pharmacology TMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany. 3. Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany. 4. DataBionics Research Group, University of Marburg, Hans-Meerwein-Straße, 35032, Marburg, Germany.
Abstract
BACKGROUND: Picturing the complexity of pain in human experimental settings has increased the predictivity for clinical pain but requires increasingly complex test batteries. This raises problems in studies in which time is objectively limited, for example by the course of action of an analgesic drug. We addressed the selection of a small yet comprehensive set of pain tests for the use in such a situation. METHOD: Nineteen different pain measures from 'classical' pain models (n = 9) and a clinically established QST-pain test battery (n = 10), were obtained from 72 healthy volunteers (34 men). The nonparametric correlation structure among the various pain measures was analysed using Ward clustering. RESULTS: Four clusters emerged, each consisting of highly correlated pain measures. The pain model groups emerged comprised (I) pain thresholds and tolerances to blunt pressure or electrical pain; (II) pain thresholds to thermal stimuli; (III) pain measures obtained following application of punctate mechanical, intranasal CO2 chemical or cutaneous laser heat stimuli; and (IV) detection thresholds to thermal stimuli. The first three clusters agreed with an immediate mechanistic interpretation as reflecting C-fibre mediated pain, thermal pain and Aδ-fibre mediated pain, respectively, whereas the last cluster contained non-painful measures and was disregarded. CONCLUSIONS: When basing a selection of a small comprehensive set of pain models on the assumption that highly correlated pain measures account for redundant results and therefore, one member of each group suffices an economic yet comprehensive pain study, results suggest inclusion of established C-fibre, Aδ-fibre mediated and thermal pain measures.
BACKGROUND: Picturing the complexity of pain in human experimental settings has increased the predictivity for clinical pain but requires increasingly complex test batteries. This raises problems in studies in which time is objectively limited, for example by the course of action of an analgesic drug. We addressed the selection of a small yet comprehensive set of pain tests for the use in such a situation. METHOD: Nineteen different pain measures from 'classical' pain models (n = 9) and a clinically established QST-pain test battery (n = 10), were obtained from 72 healthy volunteers (34 men). The nonparametric correlation structure among the various pain measures was analysed using Ward clustering. RESULTS: Four clusters emerged, each consisting of highly correlated pain measures. The pain model groups emerged comprised (I) pain thresholds and tolerances to blunt pressure or electrical pain; (II) pain thresholds to thermal stimuli; (III) pain measures obtained following application of punctate mechanical, intranasal CO2 chemical or cutaneous laser heat stimuli; and (IV) detection thresholds to thermal stimuli. The first three clusters agreed with an immediate mechanistic interpretation as reflecting C-fibre mediated pain, thermal pain and Aδ-fibre mediated pain, respectively, whereas the last cluster contained non-painful measures and was disregarded. CONCLUSIONS: When basing a selection of a small comprehensive set of pain models on the assumption that highly correlated pain measures account for redundant results and therefore, one member of each group suffices an economic yet comprehensive pain study, results suggest inclusion of established C-fibre, Aδ-fibre mediated and thermal pain measures.
Authors: Pieter S Siebenga; Guido van Amerongen; Pieter Okkerse; William S Denney; Pinky Dua; Richard P Butt; Justin L Hay; Geert J Groeneveld Journal: Eur J Pain Date: 2019-04-05 Impact factor: 3.931