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Evidence indicates that microglial G protein-coupled receptor kinase 2 (GRK2) is a key regulator of the transition from acute to chronic pain mediated by microglial products via the p38 mitogen-activated protein kinase (MAPK) pathway in the spinal cord dorsal horn (SCDH). Increasing studies have shown that autophagic dysfunction in the SCDH and neuroinflammation in the hippocampus underlie NeP. However, whether GRK2/p38MAPK and autophagic flux in the SCDH and hippocampal neuroinflammation are involved in NeP and depression comorbidity has not been determined. Here, we explored the effects of high-voltage pulsed radiofrequency (PRF) (85 V-PRF; HV-PRF) to the dorsal root ganglion (DRG) on pain phenotypes in Wistar male rats with spared nerve injury (SNI) and the underlying mechanisms. The exacerbation of pain phenotypes was markedly relieved by PRF-DRG. The SNI-induced reduction in GRK2 expression, elevation of p-p38 MAPK levels in the SCDH, and increase in IL-1ß and TNF-α levels in the hippocampus were reversed by PRF, which was accompanied by an increase in autophagic flux in spinal microglia. The beneficial effect of 85 V-PRF was superior to that of 45 V-PRF. In addition, the improvements elicited by 85 V-PRF were reversed by intrathecal injection of GRK2 antisense oligonucleotide, and these changes were accompanied by GRK2 downregulation and p-p38 upregulation in the SCDH, increased pro-inflammatory factor levels in the hippocampus, and excessive autophagy in spinal microglia. In conclusion, our data indicate that the application of HV-PRF to the DRG could serve as an excellent therapeutic technique for regulating neuroimmunity and neuroinflammation to relieve pain phenotypes.

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Objective: To compare whether falls risk score and incident fracture over 10.7 years were different among three previously identified pain phenotypes. Methods: Data on 915 participants (mean age 63 years) from a population-based cohort study were studied at baseline and follow-ups at 2.6, 5.1 and 10.7 years. Three pain phenotypes were previously identified using the latent class analysis: Class 1: high prevalence of emotional problems and low prevalence of structural damage; Class 2: high prevalence of structural damage and low prevalence of emotional problems; Class 3: low prevalence of emotional problems and low prevalence of structural damage. Fractures were self-reported and falls risk score was measured using the Physiological Profile Assessment. Generalized estimating equations model and linear mixed-effects model were used to compare differences in incident fractures and falls risk score over 10.7 years between pain phenotypes, respectively. Results: There were 3 new hip, 19 vertebral, and 121 non-vertebral fractures, and 138 any site fractures during 10.7-year follow-up. Compared with Class 3, Class 1 had a higher risk of vertebral (relative risk (RR) = 2.44, 95% CI: 1.22-4.91), non-vertebral fractures (RR = 1.20, 95% CI: 1.01-1.42), and any site fractures (RR = 1.24, 95% CI: 1.04-1.46) after controlling for covariates, bone mineral density and falls risk score. Class 2 had a higher risk of non-vertebral and any site fracture relative to those in Class 3 (non-vertebral: RR = 1.41, 95% CI: 1.17-1.71; any site: RR = 1.44, 95% CI: 1.20-1.73), but not vertebral fracture. Compared with Class 3, Class 1 had a higher falls risk score at baseline (ß = 0.16, 95% CI: 0.09-0.23) and over 10.7-year (ß = 0.03, 95% CI: 0.01-0.04). Conclusions: Class 1 and/or Class 2 had a higher risk of incident fractures and falls risk score than Class 3, highlighting that targeted preventive strategies for fractures and falls are needed in pain population.

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More than 50% of individuals develop chronic pain following traumatic brain injury (TBI). Research suggests that a significant portion of post-TBI chronic pain conditions is neuropathic in nature, yet the relationship between neuropathic pain, psychological distress, and somatosensory function following TBI is not fully understood. This study evaluated neuropathic pain symptoms, psychological and somatosensory function, and psychosocial factors in individuals with TBI (TBI, N = 38). A two-step cluster analysis was used to identify phenotypes based on the Neuropathic Pain Symptom Inventory and Beck's Anxiety Inventory scores. Phenotypes were then compared on pain characteristics, psychological and somatosensory function, and psychosocial factors. Our analyses resulted in two different neuropathic pain phenotypes: (1) Moderate neuropathic pain severity and anxiety scores (MNP-AS, N = 11); and (2) mild or no neuropathic pain symptoms and anxiety scores (LNP-AS, N = 27). Furthermore, the MNP-AS group exhibited greater depression, PTSD, pain severity, and affective distress scores than the LNP-AS group. In addition, thermal somatosensory function (difference between thermal pain and perception thresholds) was significantly lower in the MNP-AS compared to the LNP-AS group. Our findings suggest that neuropathic pain symptoms are relatively common after TBI and are not only associated with greater psychosocial distress but also with abnormal function of central pain processing pathways.

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BACKGROUND: Abdominal pain is the most distressing symptom of chronic pancreatitis (CP), and current treatments show limited benefit. Pain phenotypes may be more useful than diagnostic categories when planning treatments, and the presence or absence of constant pain in CP may be a useful prognostic indicator. AIMS: This cross-sectional study examined dimensions of pain in CP, compared pain in CP with chronic primary pain (CPP), and assessed whether constant pain in CP is associated with poorer outcomes. METHODS: Patients with CP (N = 91) and CPP (N = 127) completed the Comprehensive Pancreatitis Assessment Tool. Differences in clinical characteristics and pain dimensions were assessed between a) CP and CPP and b) CP patients with constant versus intermittent pain. Latent class regression analysis was performed (N = 192) to group participants based on pain dimensions and clinical characteristics. RESULTS: Compared to CPP, CP patients had higher quality of life (p < 0.001), lower pain severity (p < 0.001), and were more likely to use strong opioids (p < 0.001). Within CP, constant pain was associated with a stronger response to pain triggers (p < 0.05), greater pain spread (p < 0.01), greater pain severity, more features of central sensitization, greater pain catastrophising, and lower quality of life compared to intermittent pain (all p values ≤ 0.001). Latent class regression analysis identified three groups, that mapped onto the following patient groups 1) combined CPP and CP-constant, 2) majority CPP, and 3) majority CP-intermittent. CONCLUSIONS: Within CP, constant pain may represent a pain phenotype that corresponds with poorer outcomes. CP patients with constant pain show similarities to some patients with CPP, potentially indicating shared mechanisms.

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Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.

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OBJECTIVE: Knee osteoarthritis (OA) is a disease of multiple phenotypes of which a chronic pain phenotype (PP) is known. Previous PP studies have focused on one domain of pain and included heterogenous variables. We sought to identify multidimensional PPs using the IMMPACT recommendations and their relationship to clinical outcomes. METHODS: Participants >40 years of age with knee OA having a first-time orthopedic consultation at five university affiliated hospitals in Montreal, Quebec, and Hamilton (Canada) were recruited. Latent profile analysis was used to determine PPs (classes) using variables recommended by IMMPACT. This included pain variability, intensity and qualities, somatization, anxiodepressive symptoms, sleep, fatigue, pain catastrophizing, neuropathic pain, and quantitative sensory tests. We used MANOVA and χ2 tests to assess differences in participant characteristics across the classes and linear and Poisson regression to evaluate the association of classes to outcomes of physical performance tests, self-reported function and provincial healthcare data. RESULTS: In total, 343 participants were included (mean age 64 years, 64% female). Three classes were identified with increasing pain burden (class3 > class1), characterized by significant differences across most self-report measures and temporal summation, and differed in terms of female sex, younger age, lower optimism and pain self-efficacy. Participants in class2 and class3 had significantly worse self-reported function, stair climb and 40 m walk tests, and higher rates of healthcare usage compared to those in class1. CONCLUSIONS: Three distinct PPs guided by IMMPACT recommendations were identified, predominated by self-report measures and temporal summation. Using this standardized approach may improve PP study variability and comparison.

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Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. A total of 44 eligible studies were identified, with 22 presenting attempts to predict pain intensity, 15 investigating the prediction of pain phenotypes and seven assessing the prediction of treatment response. A meta-analysis was not considered appropriate for this review due to heterogeneous methods and reporting. Consequently, data were narratively synthesized. The results demonstrate that the best performing model of the individual studies allows for the prediction of pain intensity, phenotypes and treatment response with accuracies ranging between 62 to 100%, 57 to 99% and 65 to 95.24%, respectively. The results suggest that ML has the potential to effectively predict pain outcomes, which may eventually be used to assist clinical care. However, inadequate reporting and potential bias reduce confidence in the results. Future research should improve reporting standards and externally validate models to decrease bias, which would increase the feasibility of clinical translation. PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatment response from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.

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OBJECTIVE: To examine patterns of interindividual variability in experimental pain responses emerging from multiple experimental pain measures in a racially/ethnically diverse sample of healthy adults and to examine the association between the derived phenotype profiles with demographic, psychological, and health-related measures. METHODS: Two hundred and ninety-one participants underwent heat, cold, pressure, and ischemic pain assessments, and completed several psychological and health-related assessments. The experimental pain measures were subjected to a principal component analysis and factor scores were used to compute Pain Sensitivity Index scores. The scores were subsequently submitted to a cluster analysis to identify patterns of pain sensitivity across experimental pain modalities. RESULTS: The sample was equally composed of non-Hispanic whites, African Americans, and Hispanic whites. Sensitivity scores were computed for heat pain, pressure pain, cold pain, ischemic pain, and temporal summation of heat pain. Five distinct clusters were characterized by high heat pain sensitivity, low ischemic pain sensitivity, low cold pain sensitivity, low pressure pain sensitivity, and high temporal summation. Cluster membership was significantly different by sex as well as somatic reactivity and catastrophizing, although cluster differences were most pronounced between the heat pain-sensitive individuals vs the cold pain-insensitive individuals. CONCLUSIONS: Our findings highlight the importance of phenotyping individuals to account for interindividual differences in pain responses. Our findings also replicate previously reported pain phenotypes, which are not solely related to demographic, psychosocial, or health-related factors in our healthy participants. Future studies designed to elucidate the biological underpinnings of pain sensitivity profiles would be of substantial value.

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