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Diabetic polyneuropathy (DPN) is a major cause of neuropathic pain and a frequent target condition in analgesic treatment trials. Differences in the clinical symptoms and signs associated with DPN suggest distinct pathophysiological mechanisms underlying nerve damage and dysfunction that are likely to have therapeutic relevance. The aim of this study was to develop a tool for the bedside assessment of painful neuropathies such as DPN that captures the diversity of phenotypes. Sixty-one patients with type 2 diabetes and painful neuropathy, 19 patients with painless DPN, 25 patients with type 2 diabetes but no clinical evidence of neuropathy, and 20 healthy control subjects completed a structured interview (47 items) and a standardized physical examination (39 items). After analyzing critical features of pain and painless symptoms and examining the outcome of physical tests of sensory function, we determined principal components of the phenotypic variance among patients. Increased sensitivity to mechanical or thermal stimuli and, to a lesser extent, the sensory quality of pain or paresthesia were the most discriminating elements of DPN phenotypes. Correlation patterns of symptoms and signs indicated the involvement of functionally distinct nerve fiber populations. We combined interview questions and physical tests identifying these differences in a shortened assessment protocol that we named Standardized Evaluation of Pain and Somatosensory Function (StEPS). The protocol StEPS generates a phenotypic profile of patients with neuropathy. Separate intensity ratings for spontaneous painful symptoms and pain evoked by standard stimuli support a detailed documentation of neuropathic pain and its response to analgesic treatment.

Original publication

DOI

10.1097/j.pain.0000000000000648

Type

Journal article

Journal

Pain

Publication Date

10/2016

Volume

157

Pages

2297 - 2308

Keywords

Diabetes Mellitus, Type 2, Female, Humans, Hyperalgesia, Male, Middle Aged, Neuralgia, Pain Measurement, Pain Threshold, Phenotype, Principal Component Analysis, ROC Curve, Severity of Illness Index, Statistics as Topic