Biomarkers in Temporomandibular Disorder and Trigeminal Neuralgia: A Conceptual Framework for Understanding Chronic Pain

Proteomic Analysis to Identify Prospective Biomarkers of Treatment Outcome After Microvascular Decompression for Trigeminal Neuralgia: A Preliminary Study

Trigeminal neuralgia (TN) is a severe neuropathic facial pain disorder, often caused by vascular or neuronal compression of the trigeminal nerve. In such cases, microvascular decompression (MVD) surgery can be used to treat TN, but pain relief is not guaranteed. The molecular mechanisms that affect treatment response to MVD are not well understood. In this exploratory study, we performed label-free quantitative proteomic profiling of plasma and cerebrospinal fluid samples from patients undergoing MVD for TN, then compared the proteomic profiles of patients graded as responders (n = 7) versus non-responders (n = 9). We quantified 1,090 proteins in plasma and 1,087 proteins in the cerebrospinal fluid, of which 12 were differentially regulated in the same direction in both sample types. Functional analyses of differentially regulated proteins in protein-protein interaction networks suggested pathways of the immune system, axon guidance, and cellular stress response to be associated with response to MVD. These findings suggest potential biomarkers of response to MVD, as well as possible mechanisms of variable treatment success in TN patients. PERSPECTIVE: This exploratory study evaluates proteomic profiles in plasma and cerebrospinal fluid of patients undergoing microvascular decompression surgery for trigeminal neuralgia. Differential expression of proteins between surgery responders versus non-responders may serve as biomarkers to predict surgical success and provide insight into surgical mechanisms of pain relief in trigeminal neuralgia.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Brain imaging signatures of neuropathic facial pain derived by artificial intelligence

Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them from healthy controls. We conducted a retrospective analysis of diffusion tensor and T1-weighted imaging data using random forest and logistic regression AI models on 371 adults with trigeminal pain (265 classical trigeminal neuralgia (CTN), 106 trigeminal neuropathic pain (TNP)) and 108 healthy controls (HC). These models distinguished CTN from HC with up to 95% accuracy, and TNP from HC with up to 91% accuracy. Both classifiers identified gray and white matter-based predictive metrics (gray matter thickness, surface area, and volume; white matter diffusivity metrics) that significantly differed across groups. Classification of TNP and CTN did not show significant accuracy (51%) but highlighted two structures that differed between pain groups-the insula and orbitofrontal cortex. Our work demonstrates that AI models with brain imaging data alone can differentiate neuropathic facial pain subtypes from healthy data and identify regional structural indicates of pain.

The practical limits of high-quality magnetic resonance imaging for the diagnosis and classification of trigeminal neuralgia

BACKGROUND

Neurovascular compression (NVC) has been the primary hypothesis for the underlying mechanism of classical trigeminal neuralgia (TN). However, a substantial body of literature has emerged highlighting notable exceptions to this hypothesis. The purpose of this study is to assess the reliability and diagnostic accuracy of high resolution, high contrast MRI-determined neurovascular contact for TN.

METHODS

We performed a retrospective, randomized, and blinded parallel characterization of neurovascular interaction and diagnosis in a population of TN patients and controls using four expert reviewers. Performance statistics were calculated, as well as assessments for generalizability using shuffled bootstraps.

RESULTS

Fair to moderate agreement (ICC: 0.32-0.68) about diagnosis between reviewers was observed using MRIs from 47 TN patients and 47 controls. On average reviewers performed no better than chance when diagnosing participants, with an accuracy of 0.57 (95% CI 0.40, 0.59) per patient.

CONCLUSION

While MRI is useful in determining structural causes in secondary TN, expert reviewers do no better to only slightly better than chance with distinguishing TN with MRI, despite moderate agreement. Further, the causal role of NVC for TN is not clear, limiting the applicability of MRI to diagnose or prognosticate treatment of TN.

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

Chronic pain affects ~10–20% of the U.S. population with an estimated annual cost of $600 billion, the most significant economic cost of any disease to-date. Neuropathic pain is a type of chronic pain that is particularly difficult to manage and leads to significant disability and poor quality of life. Pain biomarkers offer the possibility to develop objective pain-related indicators that may help diagnose, treat, and improve the understanding of neuropathic pain pathophysiology. We review neuropathic pain mechanisms related to opiates, inflammation, and endocannabinoids with the objective of identifying composite biomarkers of neuropathic pain. In the literature, pain biomarkers typically are divided into physiological non-imaging pain biomarkers and brain imaging pain biomarkers. We review both types of biomarker types with the goal of identifying composite pain biomarkers that may improve recognition and treatment of neuropathic pain.

Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics

INTRODUCTION

Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves.

METHODS

3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability.

RESULTS

A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively.

CONCLUSION

Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN.

A comprehensive review on biomarkers associated with painful temporomandibular disorders

Pain of the orofacial region is the primary complaint for which patients seek treatment. Of all the orofacial pain conditions, one condition that possess a significant global health problem is temporomandibular disorder (TMD). Patients with TMD typically frequently complaints of pain as a symptom. TMD can occur due to complex interplay between peripheral and central sensitization, endogenous modulatory pathways, and cortical processing. For diagnosis of TMD pain a descriptive history, clinical assessment, and imaging is needed. However, due to the complex nature of pain an additional step is needed to render a definitive TMD diagnosis. In this review we explicate the role of different biomarkers involved in painful TMD. In painful TMD conditions, the role of biomarkers is still elusive. We believe that the identification of biomarkers associated with painful TMD may stimulate researchers and clinician to understand the mechanism underlying the pathogenesis of TMD and help them in developing newer methods for the diagnosis and management of TMD. Therefore, to understand the potential relationship of biomarkers, and painful TMD we categorize the biomarkers as molecular biomarkers, neuroimaging biomarkers and sensory biomarkers. In addition, we will briefly discuss pain genetics and the role of potential microRNA (miRNA) involved in TMD pain.

Trigeminal neuralgia and genetics: A systematic review

Trigeminal neuralgia (TN) is a severe facial pain disease of unknown cause and unclear genetic background. To examine the existing knowledge about genetics in TN, we performed a systematic study asking about the prevalence of familial trigeminal neuralgia, and which genes that have been identified in human TN studies and in animal models of trigeminal pain. MedLine, Embase, Cochrane Library and Web of Science were searched from inception to January 2021. 71 studies were included in the systematic review. Currently, few studies provide information about the prevalence of familial TN; the available evidence indicates that about 1–2% of TN cases have the familial form. The available human studies propose the following genes to be possible contributors to development of TN: CACNA1A, CACNA1H, CACNA1F, KCNK1, TRAK1, SCN9A, SCN8A, SCN3A, SCN10A, SCN5A, NTRK1, GABRG1, MPZ gene, MAOA gene and SLC6A4. Their role in familial TN still needs to be addressed. The experimental animal studies suggest an emerging role of genetics in trigeminal pain, though the animal models may be more relevant for trigeminal neuropathic pain than TN per se. In summary, this systematic review suggests a more important role of genetic factors in TN pathogenesis than previously assumed.

MALDI-TOF MS profiling in the discovery and identification of salivary proteomic patterns of temporomandibular joint disorders

This research aimed to identify differences in polypeptide/protein profiles of the unstimulated whole saliva between patients with temporomandibular joint disorders (TMDs) and healthy individuals. A fraction of the polypeptides/proteins (<30 kDa) was analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The spectra were recovered from methanoic acid/acetonitrile salivary samples overlaid with an α-cyano-4-hydroxy-cinnamic acid matrix in positive linear mode at an interval of 1,500–20,000 m/z mass acquisition range. The data were analyzed for the selection of characteristic peaks by using the ClinProTools 3.0 software. Discriminative classification models were generated by using a quick classifier, supervised neural network, and genetic algorithms. From the 23 peaks exhibiting the highest discriminatory power, the ten top-scored peaks with the area under the receiver operating characteristic >0.8 were selected. A panel of salivary markers that predicted the patients with TMDs was selected (2728.0, 4530.2, 5174.2, 5193.3, 6303.4, 6886.7, 8141.7, 8948.7, 10663.2, 10823.7 and 11009.0 m/z). Although carried out on relatively small datasets, the classification algorithm used in this study allows the differentiation between salivary samples from subjects with TMDs and healthy individuals and confirms the usefulness of a proteomic profiling approach in the monitoring of the disease.

Ahmad WMA, Ahmed Asif J, Khoo SP, Husein A, Ab-Ghani Z, Kassim NK. Biomarkers for Temporomandibular Disorders: Current Status and Future Directions

Numerous studies have been conducted in the previous years with an objective to determine the ideal biomarker or set of biomarkers in temporomandibular disorders (TMDs). It was recorded that tumour necrosis factor (TNF), interleukin 8 (IL-8), IL-6, and IL-1 were the most common biomarkers of TMDs. As of recently, although the research on TMDs biomarkers still aims to find more diagnostic agents, no recent study employs the biomarker as a targeting point of pharmacotherapy to suppress the inflammatory responses. This article represents an explicit review on the biomarkers of TMDs that have been discovered so far and provides possible future directions towards further research on these biomarkers. The potential implementation of the interactions of TNF with its receptor 2 (TNFR2) in the inflammatory process has been interpreted, and thus, this review presents a new hypothesis towards suppression of the inflammatory response using TNFR2-agonist. Subsequently, this hypothesis could be explored as a potential pain elimination approach in patients with TMDs.