Therapeutic potential of vagal neurostimulation to reduce food intake

迷走神经刺激减少食物摄入的治疗潜力

基本信息

批准号：
10207620
负责人：
LEE E FISHER
金额：
$ 48.22万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10207620
关键词：
Abdomen Address Adult Adverse effects Affect Anatomy Animals Behavioral Blood Pressure Body Weight Body Weight decreased Cardiovascular Diseases Cardiovascular Physiology Cervical Chemicals Chronic Classification Consumption Data Devices Diabetes Mellitus Distal Duodenum Eating Electric Stimulation Electrodes Emetics Emetine Event Excision FDA approved Feeding behaviors Ferrets Fiber Frequencies Gastric Bypass Gastrointestinal Motility Gastrointestinal Physiology Gastrointestinal tract structure Goals Gold Health Care Costs Hearing Heart Rate Implant Individual Intervention Laboratory Rat Laboratory mice Liquid substance Machine Learning Malignant Neoplasms Measures Modeling Nausea Nausea and Vomiting Non-Insulin-Dependent Diabetes Mellitus Obesity Operative Surgical Procedures Patients Pattern Pharmacology Phenotype Physiological Rattus Refractory Reporting Research Rodent Model Satiation Sensory Signal Transduction Site Sleep Stimulus Stomach Strategic Planning Surface Testing Therapeutic Training United States National Institutes of Health Upper digestive tract structure Vagus nerve structure Vomiting awake behavioral pharmacology cardiovascular effects comorbidity design effective therapy effectiveness evaluation efficacy testing experimental study feeding gastric fundus gastrointestinal gastrointestinal function healthy weight heart rate variability indexing innovation insight learning classifier machine learning algorithm obesity treatment personalized medicine pre-clinical predicting response recruit reduced food intake response side effect support vector machine therapeutic target vagus nerve stimulation weight loss intervention

项目摘要

Obesity affects almost 40% percent of US adults and is associated with high levels of comorbidities, including cancer, cardiovascular disease, and diabetes. Although effective treatments with minimal side effects are lacking, vagus nerve stimulation (VNS) can reduce body weight and suppress feeding behavior. There is little insight, however, into its mechanism and it is unclear whether VNS effects on feeding and body weight result from non-specific side effects, such as nausea. The current application directly addresses these issues by assessing gastrointestinal (GI) myoelectric changes as a potential mechanism for effects of VNS on feeding behavior, while comparing these responses to emetic activation. We plan to accomplish this by using a ferret model, which is a gold-standard for studying emesis, vagus nerve, and GI physiology. We will test the hypothesis that electrical stimulation of the vagus nerve can reduce food intake without triggering indicators of nausea, such as disrupted GI myoelectric responses, retching, and vomiting. We will complete three Aims. Aim 1: Define the individualized GI myoelectric patterns during feeding behavior using machine learning classification. Animals will be implanted with planar electrodes attached to the GI serosal surface from proximal gastric fundus to distal duodenum. We will use machine learning to classify GI myoelectric patterns of meal consumption compared to emetic-related states, including those elicited by intragastric emetine and high amplitude and frequency VNS known to trigger emesis. Aim 2: Test the efficacy of abdominal VNS on reducing meal size without triggering disruptions of GI myoelectric responses, retching, and emesis. Animals will be assessed for effects of abdominal VNS using a variety of stimulus parameters on feeding behavior and multi-site GI myoelectric recordings. Aim 3: Determine the efficacy of cervical VNS in controlling meal size without producing off-target effects (disruptions of GI myoelectric responses, retching, emesis, changes in heart rate, or blood pressure). We will test the impact of cervical VNS parameters on feeding behavior, GI myoelectric responses, retching, emesis, hear rate variability, and blood pressure. Our approach is innovative because we will use machine learning classification to detect individualized GI myoelectric response patterns in an awake free-moving animal for comparing therapeutic and off-target effects of VNS on feeding, GI activity, emesis, and cardiovascular function. This planned research is significant because VNS therapy can potentially provide a frontline treatment option for patients with high levels of obesity refractory to behavioral or pharmacological therapy, which unlike other surgical interventions for weight loss, such as gastric bypass, is potentially tunable and reversible by changing stimulation parameters, switching the device off, or complete removal.

肥胖影响着近 40% 的美国成年人，并与高水平的合并症相关，包括癌症、心血管疾病和糖尿病。尽管副作用最小的有效治疗方法如果缺乏，迷走神经刺激（VNS）可以减轻体重并抑制进食行为。很少有然而，对其机制的深入了解尚不清楚 VNS 对摄食和体重的影响是否会导致避免非特异性副作用，例如恶心。当前的应用程序直接解决了这些问题评估胃肠道 (GI) 肌电变化作为 VNS 对进食影响的潜在机制行为，同时将这些反应与催吐激活进行比较。我们计划通过使用雪貂来实现这一目标模型，这是研究呕吐、迷走神经和胃肠道生理学的黄金标准。我们将测试假设电刺激迷走神经可以减少食物摄入而不触发恶心的迹象，例如胃肠道肌电反应中断、干呕和呕吐。我们将完成三个目标。目标 1：定义进食行为期间的个体化胃肠道肌电模式机器学习分类。动物将被植入附着在胃肠道浆膜上的平面电极从近端胃底到远端十二指肠的表面。我们将使用机器学习对 GI 进行分类与催吐相关状态相比，进餐的肌电模式，包括由催吐引起的状态胃内吐司和高振幅和高频率的 VNS 已知会引发呕吐。目标2：测试功效腹部 VNS 在减少膳食量时不会引发胃肠道肌电反应、干呕、和呕吐。将使用各种刺激参数来评估动物腹部 VNS 的影响进食行为和多部位胃肠道肌电记录。目标 3：确定颈部 VNS 的疗效控制膳食量而不产生脱靶效应（胃肠道肌电反应中断、干呕、呕吐、心率或血压变化）。我们将测试颈部 VNS 参数对进食行为、胃肠道肌电反应、干呕、呕吐、心率变异性和血压。我们的方法是创新的，因为我们将使用机器学习分类来检测个性化的 GI 清醒自由活动动物的肌电反应模式，用于比较治疗效果和脱靶效果 VNS 对进食、胃肠道活动、呕吐和心血管功能的影响。这项计划研究意义重大因为 VNS 疗法有可能为高肥胖患者提供一线治疗选择与其他减肥手术干预措施不同，行为或药物治疗难以治愈，例如胃绕道手术，可以通过改变刺激参数、切换设备关闭，或完全删除。