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Chronic vagus nerve stimulation for drug-resistant epilepsy may influence fasting blood glucose concentration.

ABSTRACT: BACKGROUND:Cervical vagus nerve stimulation (VNS) has been widely accepted as adjunctive therapy for drug-resistant epilepsy and major depression. Its effects on glycemic control in humans were however poorly understood. The aim of our study was to investigate the potential effects of VNS on fasting blood glucose (FBG) in patients with drug-resistant epilepsy. METHODS:Patients with drug-resistant epilepsy who had received VNS implants at the same hospital were retrospectively studied. Effects on FBG, weight, body mass index and blood pressure were evaluated at 4, 8 and 12 months of follow-up. RESULTS:32 subjects (11 females/21 males, 19?±?9 years, body mass index 22.2?±?4.0 kg/m2) completed 12-month follow-up. At the 4 months, there were no significant changes in FBG concentrations from baseline to follow-up in both Sham-VNS (4.89?±?0.54 vs. 4.56?±?0.54 mmol/L, N?=?13, p?=?0.101) and VNS (4.80?±?0.54 vs. 4.50?±?0.56 mmol/L, N?=?19, p?=?0.117) groups. However, after 8 (4.90?±?0.42 mmol/L, N?=?32, p?=?0.001) and 12 (4.86?±?0.40 mmol/L, N?=?32, p?=?0.002) months of VNS, FBG levels significantly increased compared to baseline values (4.52?±?0.54 mmol/L, N?=?32). Changes in FBG concentrations at both 8 (R2?=?0.502, N?=?32, p?

SUBMITTER: Liu H

PROVIDER: S-EPMC7257242 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Chronic vagus nerve stimulation for drug-resistant epilepsy may influence fasting blood glucose concentration.

Liu Hongyun H Zhan Ping P Meng Fangang F Wang Weidong W

Biomedical engineering online 20200529 1

<h4>Background</h4>Cervical vagus nerve stimulation (VNS) has been widely accepted as adjunctive therapy for drug-resistant epilepsy and major depression. Its effects on glycemic control in humans were however poorly understood. The aim of our study was to investigate the potential effects of VNS on fasting blood glucose (FBG) in patients with drug-resistant epilepsy.<h4>Methods</h4>Patients with drug-resistant epilepsy who had received VNS implants at the same hospital were retrospectively stud ...[more]

PMID: 32471438

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Project description:Introduction: VNS is an adjunctive neuromodulation therapy for patients with drug-refractory epilepsy. The antiseizure effect of VNS is thought to be related to a diffuse modulation of functional connectivity but remains to be confirmed. Aim: To investigate electroencephalographic (EEG) metrics of functional connectivity in patients with drug-refractory epilepsy treated by vagus nerve stimulation (VNS), between VNS-stimulated "ON" and nonstimulated "OFF" periods and between responder (R) and nonresponder (NR) patients. Methods: Scalp-EEG was performed for 35 patients treated by VNS, using 21 channels and 2 additional electrodes on the neck to detect the VNS stimulation. Patients were defined as VNS responders if a reduction of seizure frequency of ∼50% was documented. We analyzed the synchronization in EEG time series during "ON" and "OFF" periods of stimulation, using average phase lag index (PLI) in signal space and phase-locking value (PLV) between 10 sources. Based on graph theory, we computed brain network models and analyzed minimum spanning tree (MST) for responder and nonresponder patients. Results: Among 35 patients treated by VNS for a median time of 7 years (range 4 months to 22 years), 20 were R and 15 were NR. For responder patients, PLI during ON periods was significantly lower than that during OFF periods in delta (p = 0.009), theta (p = 0.02), and beta (p = 0.04) frequency bands. For nonresponder patients, there were no significant differences between ON and OFF periods. Moreover, variations of seizure frequency with VNS correlated with the PLI OFF/ON ratio in delta (p = 0.02), theta (p = 0.04), and beta (p = 0.03) frequency bands. Our results were confirmed using PLV in theta band (p < 0.05). No significant differences in MST were observed between R and NR patients. Conclusion: The correlation between VNS-induced interictal EEG time-series desynchronization and decrease in seizure frequency suggested that VNS therapeutic impact might be related to changes in interictal functional connectivity. Impact statement Electroencephalography (EEG) desynchronization has been proposed to be a mechanism for antiepileptic effect of vagus nerve stimulation (VNS). We measured interictal EEG time-series synchronization during stimulated (ON) and nonstimulated (OFF) periods in epileptic patients treated by VNS. Phase lag index differences between ON and OFF periods were measured in delta, theta, and beta bands only in responder patients. To our knowledge, our study is the first to statistically correlate interictal cortical desynchronization during ON periods with reduction in seizure frequency. Our result supports the hypothesis that the antiseizure effect of VNS is mediated by cortical desynchronization.

Project description:ObjectiveTo investigate executive functions and attention with repeated EpiTrack evaluations in a group of DR patients with drug-resistant epilepsy (DRE) receiving vagus nerve stimulation (VNS) during a follow-up duration of up to 5 years.MethodsThe study involved 33 patients with DRE who were assessed with EpiTrack as a part of the clinical VNS protocol. Evaluations were scheduled prior to VNS implantation and then at 6 months, 12 months, and yearly thereafter. However, the COVID-19 pandemic disrupted follow-up. Therefore, changes in EpiTrack total scores over time were analyzed using a linear mixed-effects (LMEs) model to compensate for the variation in follow-up duration when predicting EpiTrack total score changes over 5 years.ResultsThe median follow-up time was 29 months. During each month, the EpiTrack total score was predicted to increase by 0.07 units (95% confidence interval [CI]: 0.01-0.12, P = 0.02), corresponding to a change from a baseline score of 27.3 (severe impairment) to a score of 28.9 (mild impairment) at 2 years and a score of 31.5 (almost normal) at 5 years. In the group of patients with psychiatric comorbidities, the EpiTrack total score increased by 0.14 units per month (P = 0.003), which was 3.5-fold higher than the increase of patients without psychiatric comorbidities. For the patients taking 1-2 antiseizure medications (ASMs), the EpiTrack total score increased by 0.11 units per month (P = 0.005), which was almost quadruple the rate of patients taking 3-4 ASMs.SignificanceBased on EpiTrack total scores, the LME model predicted a four-point improvement in executive functions among patients with DRE at 5 years after the initiation of VNS, representing a clinically meaningful change. DRE patients with comorbid depression seemed to experience the most cognitive benefits. In addition, better cognitive outcomes were achieved if the patient took less than three ASMs.Plain language summaryExecutive functions and attention may improve during vagus nerve stimulation therapy in patients with drug-resistant epilepsy. Epilepsy patients who have depression or use fewer than three antiseizure medications are likely to benefit cognitively more from the treatment.

Project description:Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices. Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model. Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1). Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

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Chronic vagus nerve stimulation for drug-resistant epilepsy may influence fasting blood glucose concentration.

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Chronic vagus nerve stimulation for drug-resistant epilepsy may influence fasting blood glucose concentration.

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