Association of Epileptic and Nonepileptic Seizures and Changes in Circulating Plasma Proteins Linked to Neuroinflammation

Abstract

Objective: To develop a diagnostic test that stratifies epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES) by developing a multimodal algorithm that integrates plasma concentrations of selected immune response-associated proteins and patient clinical risk factors for seizure.

Methods: Daily blood samples were collected from patients evaluated in the epilepsy monitoring unit within 24 hours after EEG confirmed ES or PNES and plasma was isolated. Levels of 51 candidate plasma proteins were quantified using an automated, multiplexed, sandwich ELISA and then integrated and analyzed using our diagnostic algorithm.

Results: A 51-protein multiplexed ELISA panel was used to determine the plasma concentrations of patients with ES, patients with PNES, and healthy controls. A combination of protein concentrations, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), intercellular adhesion molecule 1 (ICAM-1), monocyte chemoattractant protein-2 (MCP-2), and tumor necrosis factor-receptor 1 (TNF-R1) indicated a probability that a patient recently experienced a seizure, with TRAIL and ICAM-1 levels higher in PNES than ES and MCP-2 and TNF-R1 levels higher in ES than PNES. The diagnostic algorithm yielded an area under the receiver operating characteristic curve (AUC) of 0.94 ± 0.07, sensitivity of 82.6% (95% confidence interval [CI] 62.9-93.0), and specificity of 91.6% (95% CI 74.2-97.7). Expanding the diagnostic algorithm to include previously identified PNES risk factors enhanced diagnostic performance, with AUC of 0.97 ± 0.05, sensitivity of 91.3% (95% CI 73.2-97.6), and specificity of 95.8% (95% CI 79.8-99.3).

Conclusions: These 4 plasma proteins could provide a rapid, cost-effective, and accurate blood-based diagnostic test to confirm recent ES or PNES.

Classification of evidence: This study provides Class III evidence that variable levels of 4 plasma proteins, when analyzed by a diagnostic algorithm, can distinguish PNES from ES with sensitivity of 82.6% and specificity of 91.6%.

Figure 1. Patient Inclusion Flowchart

All-comers enrollment was 137 and through a retrospective analysis resulted in cohort sizes of 23 patients with epileptic seizures (ES) and 24 patients with psychogenic nonepileptic seizures (PNES). Patients were excluded if they did not experience a diagnostic event in the epilepsy monitoring unit (EMU) and if they did not contribute blood within 24 hours after a qualifying event (e.g., ES or PNES event).

Figure 2. Comparison of Protein Concentrations for the Epileptic Seizures (ES) and Psychogenic Nonepileptic Seizures (PNES) Cohorts

(A) Cohen d effect size was used to explore differences in mean concentrations between the ES and PNES cohorts. The bar plot shows the effect size and 95% confidence interval of each protein sorted in descending order. (B) Select proteins demonstrate the magnitude of changes observed between cohorts. This figure also shows how noncanonical responses are observed for inflammation-related proteins, such as tumor necrosis factor–related apoptosis-inducing ligand (TRAIL) and intercellular adhesion molecule 1 (ICAM-1), which can be used to identify patients experiencing a seizure. CRP = C-reactive protein; IL = interleukin; IP-10 = IFN-γ-inducible protein 10; MCP = monocyte chemoattractant protein; MIP = macrophage inflammatory protein; MMP = matrix metalloproteinase; SAA = serum amyloid protein A; SCF = stem cell factor; TARC = thymus and activation‐regulated chemokine; TNF-R1 = tumor necrosis factor–receptor 1; TNF-R2 = tumor necrosis factor–receptor 2; VCAM = vascular cell adhesion molecule; VEGF = vascular endothelial growth factor.

Figure 3. Epileptic Seizures (ES)/Psychogenic Nonepileptic Seizures (PNES) Diagnostic Algorithm Performance Refined Using Selected Protein Concentrations

Performance of a diagnostic algorithm that incorporates tumor necrosis factor–related apoptosis-inducing ligand, intercellular adhesion molecule 1, monocyte chemoattractant protein 2, and tumor necrosis factor–receptor 1 protein concentrations. The algorithm was refined using a logistic regression. (A) The score distribution for ES and PNES cohorts shown as a bar and whisker plot using the Tukey method. (B) The performance matrix and (C) the receiver operating characteristic (ROC) curve demonstrate a good tradeoff between sensitivity and specificity with an area under the ROC of 0.94. These results demonstrate the algorithms’ ability to stratify patients with ES from those with PNES. NPV = negative predictive value; PPV = positive predictive value.

Figure 4. Epileptic Seizures/Psychogenic Nonepileptic Seizures (PNES) Diagnostic Algorithm That Integrates Both Clinical Risk Factors and Protein Concentrations

(A, B) Performance characteristics of a diagnostic algorithm that was refined using both the plasma concentration of tumor necrosis factor–related apoptosis-inducing ligand, intercellular adhesion molecule 1, monocyte chemoattractant protein 2, and tumor necrosis factor–receptor 1 and the sum of PNES risk factors. Risk factors include major depressive disorder, posttraumatic stress disorder, cluster B personality disorder, multiple allergies, conversion disorder, sex, fibromyalgia, migraine, pain, and asthma. (C, D) Performance of a diagnostic algorithm that was refined using only the PNES risk factors listed above. AUC = area under the receiver operating characteristic curve; NPV = negative predictive value; PPV = positive predictive value.