Toward Composite Pain Biomarkers of Neuropathic Pain—Focus on Peripheral Neuropathic Pain

Elevated Biomarkers of Inflammation and Vascular Dysfunction Are Associated with Distal Sensory Polyneuropathy in People with HIV

Distal sensory polyneuropathy (DSP) is a disabling, chronic condition in people with HIV (PWH), even those with viral suppression of antiretroviral therapy (ART), and with a wide range of complications, such as reduced quality of life. Previous studies demonstrated that DSP is associated with inflammatory cytokines in PWH. Adhesion molecules, essential for normal vascular function, are perturbed in HIV and other conditions linked to DSP, but the link between adhesion molecules and DSP in PWH is unknown. This study aimed to determine whether DSP signs and symptoms were associated with a panel of plasma biomarkers of inflammation (d-dimer, sTNFRII, MCP-1, IL-6, IL-8, IP-10, sCD14) and vascular I integrity (ICAM-1, VCAM-1, uPAR, MMP-2, VEGF, uPAR, TIMP-1, TIMP-2) and differed between PWH and people without HIV (PWoH). A cross-sectional study was conducted among 143 participants (69 PWH and 74 PWoH) assessed by studies at the UC San Diego HIV Neurobehavioral Research Program. DSP signs and symptoms were clinically assessed for all participants. DSP was defined as two or more DSP signs: bilateral symmetrically reduced distal vibration, sharp sensation, and ankle reflexes. Participant-reported symptoms were neuropathic pain, paresthesias, and loss of sensation. Factor analyses reduced the dimensionality of the 15 biomarkers among all participants, yielding six factors. Logistic regression was used to assess the associations between biomarkers and DSP signs and symptoms, controlling for relevant demographic and clinical covariates. The 143 participants were 48.3% PWH, 47 (32.9%) women, and 47 (33.6%) Hispanics, with a mean age of 44.3 ± 12.9 years. Among PWH, the median (IQR) nadir and current CD4+ T-cells were 300 (178-448) and 643 (502-839), respectively. Participants with DSP were older but had similar distributions of gender and ethnicity to those without DSP. Multiple logistic regression showed that Factor 2 (sTNFRII and VCAM-1) and Factor 4 (MMP-2) were independently associated with DSP signs in both PWH and PWoH (OR [95% CI]: 5.45 [1.42-21.00], and 15.16 [1.07-215.22]), respectively. These findings suggest that inflammation and vascular integrity alterations may contribute to DSP pathogenesis in PWH, but not PWoH, possibly through endothelial dysfunction and axonal degeneration.

Is burning mouth syndrome associated with stress? A meta-analysis

BACKGROUND

Some studies have shown burning mouth syndrome (BMS) as comorbid psychosocial and psychiatric disorders, and as well, pointed at stress as a major risk factor.

OBJECTIVE

The aim of this meta-analysis was to answer the following question: 'Is there an association between BMS and stress, compared to healthy controls?'

METHODS

Two reviewers searched for the effect of stress in BMS and published on five main databases and three from the grey literature. Various questionnaires and biomarkers were analysed. Of the 2489 selected articles, 30 met the inclusion criteria. Studies englobed questionnaires, such as Perceived Stress Questionnaire, Lipp Stress Symptoms Inventory, Holmes-Rahe scale, Depression, Anxiety, and Stress Scale (DASS-21), Recent Experience Test; and various biomarkers, such as cortisol, opiorphin, IgA, α-amylase and interleukins.

RESULTS

In all studies with questionnaires, stress was significantly increased in the BMS group vs. control. Patients with BMS presented 25.73% higher cortisol levels, 28.17% higher IgA levels and 40.62% higher α-amylase levels than controls. Meta-analysis found that BMS subjects presented 3.01 nmoL/L [0.53; 5.50] higher cortisol levels, 84.35 kU/L [15.00; 153.71] higher α-amylase levels, 29.25 mg/mL [9.86; 48.64] higher IgA levels and 258.59 pg/mL [59.24; 457.94] higher IL-8 levels than control. No differences were found for opiorphin concentration in ng/mL [-0.96; 2.53]. For interleukins, no differences were founded for IL-1 β, IL-2, IL-4, IL-6, IL-8, IL-10 and TNF-α.

CONCLUSION

Based on the available evidence, this meta-analysis suggests more stress factors in questionnaire-based studies, and higher levels of cortisol, α-amylase, IgA and IL-8 biomarkers in BMS subjects than controls.

Microstructural Changes in the Spinothalamic Tract of CPSS Patients: Preliminary Results from a Single-Center Diffusion-Weighted Magnetic Resonance Imaging Study

INTRODUCTION

Chronic pain after spinal surgery (CPSS), formerly known as failed back surgery syndrome, encompasses a variety of highly incapacitating chronic pain syndromes emerging after spinal surgery. The intractability of CPSS makes objective parameters that could aid classification and treatment essential. In this study, we investigated the use of cerebral diffusion-weighted magnetic resonance imaging.

METHODS

Cerebral 3T diffusion-weighted (DW-) MRI data from adult CPSS patients were assessed and compared with those of healthy controls matched by age and gender. Only imaging data without relevant artefacts or significant pathologies were included. Apparent diffusion coefficient (ADC) maps were calculated from the b0 and b1000 values using nonlinear regression. After skull stripping and affine registration of all imaging data, ADC values for fifteen anatomical regions were calculated and analyzed with independent samples T-tests.

RESULTS

A total of 32 subjects were included (sixteen CPSS patients and sixteen controls). The mean ADC value of the spinothalamic tract was found to be significantly higher in CPSS patients compared with in healthy controls (p = 0.013). The other anatomical regions did not show statistically different ADC values between the two groups.

CONCLUSION

Our results suggest that patients suffering from CPSS are subject to microstructural changes, predominantly within the cerebral spinothalamic tract. Additional research could possibly lead to imaging biomarkers derived from ADC values in CPSS patients.

Alpha-2 macroglobulin for the treatment of neuroma pain in the stump of a below-knee amputee patient

This case report describes the successful treatment of neuroma pain in the setting of below knee amputations using alpha-2-macroglobulin (A2M). A 34-year-old female patient presented with 9 months of stump pain despite conservative treatment. The exam revealed persistent pain through rest periods and weight-bearing status during therapy. Ultrasound showed neuroma formation with neovascularization. The patient underwent two A2M hydrodissection treatments, 2 weeks apart. The patient reported significant pain relief. Ultrasound showed decreases in neovascularization and cross-sectional area of the neuroma. The patient was able to ambulate pain-free for 2 years and reported no pain since. A2M may be a treatment for patients with neuroma pain in the setting of amputations.

A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

BACKGROUND

The identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs).

OBJECTIVE

This study aimed to develop and evaluate a scalable natural language processing (NLP)- and radiomics-based machine learning pipeline to differentiate between painless and painful BM lesions in simulation computed tomography (CT) images using imaging features (biomarkers) extracted from lesion center point-based regions of interest (ROIs).

METHODS

Patients treated at our comprehensive cancer center who received palliative radiotherapy for thoracic spine BM between January 2016 and September 2019 were included in this retrospective study. Physician-reported pain scores were extracted automatically from radiation oncology consultation notes using an NLP pipeline. BM center points were manually pinpointed on CT images by radiation oncologists. Nested ROIs with various diameters were automatically delineated around these expert-identified BM center points, and radiomics features were extracted from each ROI. Synthetic Minority Oversampling Technique resampling, the Least Absolute Shrinkage And Selection Operator feature selection method, and various machine learning classifiers were evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve.

RESULTS

Radiation therapy consultation notes and simulation CT images of 176 patients (mean age 66, SD 14 years; 95 males) with thoracic spine BM were included in this study. After BM center point identification, 107 radiomics features were extracted from each spherical ROI using pyradiomics. Data were divided into 70% and 30% training and hold-out test sets, respectively. In the test set, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of our best performing model (neural network classifier on an ensemble ROI) were 0.82 (132/163), 0.59 (16/27), 0.85 (116/136), and 0.83, respectively.

CONCLUSIONS

Our NLP- and radiomics-based machine learning pipeline was successful in differentiating between painful and painless BM lesions. It is intrinsically scalable by using NLP to extract pain scores from clinical notes and by requiring only center points to identify BM lesions in CT images.

Unsupervised learning for prognostic validity in patients with chronic pain in transdisciplinary pain care

Chronic pain is not a singular disorder and presents in various forms and phenotypes. Here we show data from a cohort of patients seeking treatment in a transdisciplinary pain clinic. Patients completed a multidimensional patient-reported battery as part of routine initial evaluation at baseline and at each of the four subsequent visits over 1-year follow-up (0, 1, 3, 6, 12 months). The goal of this work was to use unsupervised modeling approach to identify whether patients with chronic pain undergoing transdisciplinary intensive rehabilitation treatment: (1) can be derived based upon self-reported outcome measures at baseline (or before treatment initiation), (2) are clinically validated based on their clinical diagnosis and medication use, and (3) differ in treatment trajectories over 1 year of transdisciplinary treatment. We applied unsupervised clustering on baseline outcomes using nine patient-reported symptoms and examined treatment trajectories. The three-cluster solution was internally validated. Psychiatric diagnosis, chronic back pain-related disability and symptoms severity determined cluster assignment and treatment prognosis. Conversely, clinical pain severity had lesser effect. Furthermore, clusters showed stability over time despite symptoms improvement. The accurate and meaningful subgrouping of the underlying chronic pain phenotypes would greatly enhance treatment and provide personalized and effective pain management.

The Identification of Human Translational Biomarkers of Neuropathic Pain and Cross-Species Validation Using an Animal Model

Neuropathic pain is a common chronic condition, which remains poorly understood. Many patients receiving treatment continue to experience severe pain, due to limited diagnostic/treatment management programmes. The development of objective clinical diagnostic/treatment strategies requires identification of robust biomarkers of neuropathic pain. To this end, we looked to identify biomarkers of chronic neuropathic pain by assessing gene expression profiles in an animal model of neuropathic pain, and differential gene expression in patients to determine the potential translatability. We demonstrated cross-species validation of several genes including those identified through bioinformatic analysis by assessing their expression in blood samples from neuropathic pain patients, according to conservative assessments of significance measured using Bonferroni-corrected p-values. These include CASP5 (p = 0.00226), CASP8 (p = 0.00587), CASP9 (p = 2.09 × 10^-9), FPR2 (p = 0.00278), SH3BGRL3 (p = 0.00633), and TMEM88 (p = 0.00038). A ROC analysis revealed several combinations of genes to show high levels of discriminatory power in the comparison of neuropathic pain patients and control participants, of which the combination SH3BGRL3, TMEM88, and CASP9 achieved the highest level (AUROC = 0.923). The CASP9 gene was found to be common in five combinations of three genes revealing the highest levels of discriminatory power. In contrast, the gene combination PLAC8, ROMO1, and A3GALT2 showed the highest levels of discriminatory power in the comparison of neuropathic pain and nociceptive pain (AUROC = 0.919), when patients were grouped by S-LANSS scores. Molecules that demonstrate an active role in neuropathic pain have the potential to be developed into a biological measure for objective diagnostic tests, or as novel drug targets for improved pain management.

The Roles of Imaging Biomarkers in the Management of Chronic Neuropathic Pain

Chronic neuropathic pain (CNP) affects around 10% of the general population and has a significant social, emotional, and economic impact. Current diagnosis techniques rely mainly on patient-reported outcomes and symptoms, which leads to significant diagnostic heterogeneity and subsequent challenges in management and assessment of outcomes. As such, it is necessary to review the approach to a pathology that occurs so frequently, with such burdensome and complex implications. Recent research has shown that imaging methods can detect subtle neuroplastic changes in the central and peripheral nervous system, which can be correlated with neuropathic symptoms and may serve as potential markers. The aim of this paper is to review available imaging methods used for diagnosing and assessing therapeutic efficacy in CNP for both the preclinical and clinical setting. Of course, further research is required to standardize and improve detection accuracy, but available data indicate that imaging is a valuable tool that can impact the management of CNP.

Salivary Endocannabinoid Profiles in Chronic Orofacial Pain and Headache Disorders: An Observational Study Using a Novel Tool for Diagnosis and Management

The endocannabinoid system is involved in physiological and pathological processes, including pain generation, modulation, and sensation. Its role in certain types of chronic orofacial pain (OFP) has not been thoroughly examined. By exploring the profiles of specific salivary endocannabinoids (eCBs) in individuals with different types of OFP, we evaluated their use as biomarkers and the influence of clinical parameters and pain characteristics on eCB levels. The salivary levels of anandamide (AEA), 2-arachidonoyl glycerol (2-AG), and their endogenous breakdown product arachidonic acid (AA), as well as the eCB-like molecules N-palmitoylethanolamide (PEA) and N-oleoylethanolamide (OEA), were assessed in 83 OFP patients and 43 pain-free controls using liquid chromatography/tandem mass spectrometry. Patients were grouped by diagnosis: post-traumatic neuropathy (PTN), trigeminal neuralgia (TN), temporomandibular disorder (TMD), migraine, tension-type headache (TTH), and burning mouth syndrome (BMS). Correlation analyses between a specific diagnosis, pain characteristics, and eCB levels were conducted. Significantly lower levels of 2-AG were found in the TN and TTH groups, while significantly lower PEA levels were found in the migraine group. BMS was the only group with elevated eCBs (AEA) versus the control. Significant correlations were found between levels of specific eCBs and gender, health-related quality of life (HRQoL), BMI, pain duration, and sleep awakenings. In conclusion, salivary samples exhibited signature eCBs profiles for major OFP disorders, especially migraine, TTH, TN, and BMS. This finding may pave the way for using salivary eCBs biomarkers for more accurate diagnoses and management of chronic OFP patients.

Experimental and Clinical Biomarkers for Progressive Evaluation of Neuropathology and Therapeutic Interventions for Acute and Chronic Neurological Disorders

This article describes commonly used experimental and clinical biomarkers of neuronal injury and neurodegeneration for the evaluation of neuropathology and monitoring of therapeutic interventions. Biomarkers are vital for diagnostics of brain disease and therapeutic monitoring. A biomarker can be objectively measured and evaluated as a proxy indicator for the pathophysiological process or response to therapeutic interventions. There are complex hurdles in understanding the molecular pathophysiology of neurological disorders and the ability to diagnose them at initial stages. Novel biomarkers for neurological diseases may surpass these issues, especially for early identification of disease risk. Validated biomarkers can measure the severity and progression of both acute neuronal injury and chronic neurological diseases such as epilepsy, migraine, Alzheimer's disease, Parkinson's disease, Huntington's disease, traumatic brain injury, amyotrophic lateral sclerosis, multiple sclerosis, and other brain diseases. Biomarkers are deployed to study progression and response to treatment, including noninvasive imaging tools for both acute and chronic brain conditions. Neuronal biomarkers are classified into four core subtypes: blood-based, immunohistochemical-based, neuroimaging-based, and electrophysiological biomarkers. Neuronal conditions have progressive stages, such as acute injury, inflammation, neurodegeneration, and neurogenesis, which can serve as indices of pathological status. Biomarkers are critical for the targeted identification of specific molecules, cells, tissues, or proteins that dramatically alter throughout the progression of brain conditions. There has been tremendous progress with biomarkers in acute conditions and chronic diseases affecting the central nervous system.