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Objectives Despite increasing evidence demonstrating structural and functional alterations within the central nervous system in diabetic peripheral neuropathy (DPN), the neuroanatomical correlates of Painful- and Painless-DPN have yet to be identified. Focusing on structural magnetic resonance (MR) imaging, the aims of this study were to 1) define the brain morphological alterations in Painful- and Painless-DPN and 2) explore the relationships between brain morphology and clinical/neurophysiological assessments. Research Design and Methods 277 participants with type 1 and 2 diabetes (No-DPN [n=57]; Painless-DPN [n=77]; and Painful-DPN [n=77]) and 66 healthy volunteers (HV) were enrolled. All underwent detailed clinical/neurophysiological assessment and brain MR imaging at 3-Tesla. Painful-DPN participants were subdivided into the irritable nociceptor (IR) and the non-irritable nociceptor (NIR) phenotype using the German Research Network on Neuropathic Painprotocol. Cortical reconstruction and volumetric segmentation were performed with FreeSurfer software and voxel-based morphometry implemented in FSL. Results Both Painful- and Painless-DPN had significant reduction in primary somatosensory and motor cortical thickness compared to HV and No-DPN [P=0.02; F(3,275)=3.36 and P=0.01; F(3,275)=3.80 respectively]. Somatomotor cortical thickness correlated with neurophysiological measures of DPN severity. There was also reduction in ventrobasal thalamic nuclei volume in both Painless- and Painful-DPN. Painful-DPN subjects with the NIR nociceptor phenotype had reduced primary somatosensory, posterior cingulate cortical and thalamic volume compared to the IR nociceptor phenotype. Conclusions In this, the largest neuroimaging study in DPN to date, we have demonstrated significant structural alterations in key somatomotor/nociceptive brain regions specific to Painless-DPN and Painful-DPN including the IR and NIR nociceptor phenotype.

Aims/hypothesis The aim of this work was to investigate whether different clinical pain phenotypes of diabetic polyneuropathy (DPN) are distinguished by functional connectivity at rest. Methods This was an observational, cohort study of 43 individuals with painful DPN, divided into irritable (IR, n = 10) and non-irritable (NIR, n = 33) nociceptor phenotypes using the German Research Network of Neuropathic Pain quantitative sensory testing protocol. In-situ brain MRI included 3D T1-weighted anatomical and 6 min resting-state functional MRI scans. Subgroup differences in resting-state functional connectivity in brain regions involved with somatic (thalamus, primary somatosensory cortex, motor cortex) and non-somatic (insular and anterior cingulate cortices) pain processing were examined. Multidimensional reduction of MRI datasets was performed using a machine-learning approach to classify individuals into each clinical pain phenotype. Results Individuals with the IR nociceptor phenotype had significantly greater thalamic–insular cortex (p false discovery rate [FDR] = 0.03) and reduced thalamus–somatosensory cortex functional connectivity (p-FDR = 0.03). We observed a double dissociation such that self-reported neuropathic pain score was more associated with greater thalamus–insular cortex functional connectivity (r = 0.41; p = 0.01) whereas more severe nerve function deficits were more related to lower thalamus–somatosensory cortex functional connectivity (r = −0.35; p = 0.03). Machine-learning group classification performance to identify individuals with the NIR nociceptor phenotype achieved an accuracy of 0.92 (95% CI 0.08) and sensitivity of 90%. Conclusions/interpretation This study demonstrates differences in functional connectivity in nociceptive processing brain regions between IR and NIR phenotypes in painful DPN. We also establish proof of concept for the utility of multimodal MRI as a biomarker for painful DPN by using a machine-learning approach to classify individuals into sensory phenotypes. Graphical abstract

Painful diabetic polyneuropathy (DPN) is difficult to manage as treatment response is often varied. The primary aim of this study was to examine differences in pain phenotypes between responders and non-responders to intravenous lidocaine treatment using quantitative sensory testing. The secondary aim was to explore differences in brain structure and functional connectivity with treatment response. Forty-five consecutive patients who received intravenous lidocaine treatment for painful DPN were screened. Twenty-nine patients who met the eligibility criteria (responders n=14 and non-responders n=15) and 26 healthy controls underwent detailed sensory profiling. Subjects also underwent multimodal brain magnetic resonance (MR) imaging. Greater proportion of patients with the irritable (IR) nociceptor phenotype were responders to intravenous lidocaine treatment compared to non-responders . The odd-ratio of responding to intravenous lidocaine was 8.67 times greater (95%CI 1.4:53.8) for the IR nociceptor phenotype. Responders to intravenous lidocaine also had significantly greater mean S1 cortical volume and functional connectivity between the insula cortex and the corticolimbic circuitry. This study provides preliminary evidence for a mechanism-based approach for individualising therapy in patients with painful DPN.

Supplemental Digital Content is Available in the Text. Phenotype stratification of patients with peripheral neuropathic pain can be conducted with a novel algorithm based on sensory profiles., In a recent cluster analysis, it has been shown that patients with peripheral neuropathic pain can be grouped into 3 sensory phenotypes based on quantitative sensory testing profiles, which are mainly characterized by either sensory loss, intact sensory function and mild thermal hyperalgesia and/or allodynia, or loss of thermal detection and mild mechanical hyperalgesia and/or allodynia. Here, we present an algorithm for allocation of individual patients to these subgroups. The algorithm is nondeterministic—ie, a patient can be sorted to more than one phenotype—and can separate patients with neuropathic pain from healthy subjects (sensitivity: 78%, specificity: 94%). We evaluated the frequency of each phenotype in a population of patients with painful diabetic polyneuropathy (n = 151), painful peripheral nerve injury (n = 335), and postherpetic neuralgia (n = 97) and propose sample sizes of study populations that need to be screened to reach a subpopulation large enough to conduct a phenotype-stratified study. The most common phenotype in diabetic polyneuropathy was sensory loss (83%), followed by mechanical hyperalgesia (75%) and thermal hyperalgesia (34%, note that percentages are overlapping and not additive). In peripheral nerve injury, frequencies were 37%, 59%, and 50%, and in postherpetic neuralgia, frequencies were 31%, 63%, and 46%. For parallel study design, either the estimated effect size of the treatment needs to be high (>0.7) or only phenotypes that are frequent in the clinical entity under study can realistically be performed. For crossover design, populations under 200 patients screened are sufficient for all phenotypes and clinical entities with a minimum estimated treatment effect size of 0.5.

Disabling neuropathic pain (NeuP) is a common sequel of diabetic peripheral neuropathy (DPN). We aimed to characterise the sensory phenotype of patients with and without NeuP, assess screening tools for NeuP, and relate DPN severity to NeuP. The Pain in Neuropathy Study (PiNS) is an observational cross-sectional multicentre study. A total of 191 patients with DPN underwent neurological examination, quantitative sensory testing, nerve conduction studies, and skin biopsy for intraepidermal nerve fibre density assessment. A set of questionnaires assessed the presence of pain, pain intensity, pain distribution, and the psychological and functional impact of pain. Patients were divided according to the presence of DPN, and thereafter according to the presence and severity of NeuP. The DN4 questionnaire demonstrated excellent sensitivity (88%) and specificity (93%) in screening for NeuP. There was a positive correlation between greater neuropathy severity (r = 0.39, P < 0.01), higher HbA1c (r = 0.21, P < 0.01), and the presence (and severity) of NeuP. Diabetic peripheral neuropathy sensory phenotype is characterised by hyposensitivity to applied stimuli that was more marked in the moderate/severe NeuP group than in the mild NeuP or no NeuP groups. Brush-evoked allodynia was present in only those with NeuP (15%); the paradoxical heat sensation did not discriminate between those with (40%) and without (41.3%) NeuP. The “irritable nociceptor” subgroup could only be applied to a minority of patients (6.3%) with NeuP. This study provides a firm basis to rationalise further phenotyping of painful DPN, for instance, stratification of patients with DPN for analgesic drug trials.

Clinical methods of detecting diabetic peripheral neuropathy (DPN) are not objective and reproducible. We therefore evaluated if SUDOSCAN, a new method developed to provide a quick, non-invasive and quantitative assessment of sudomotor function can reliably screen for DPN. 70 subjects (45 with type 1 diabetes and 25 healthy volunteers [HV]) underwent detailed assessments including clinical, neurophysiological and 5 standard cardiovascular reflex tests (CARTs). Using the American Academy of Neurology criteria subjects were classified into DPN and No-DPN groups. Based on CARTs subjects were also divided into CAN, subclinical-CAN and no-CAN. Sudomotor function was assessed with measurement of hand and foot Electrochemical Skin Conductance (ESC) and calculation of the CAN risk score. Foot ESC (μS) was significantly lower in subjects with DPN [n = 24; 53.5(25.1)] compared to the No-DPN [77.0(7.9)] and HV [77.1(14.3)] groups (ANCOVA p