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.

肥胖症影响了几乎40％的美国成年人，并且与高水平的合并症有关，包括癌症，心血管疾病和糖尿病。尽管有效的副作用的有效治疗方法是缺乏迷走神经刺激（VN）可以减轻体重并抑制喂养行为。几乎没有然而，洞察力对其机制，尚不清楚VNS对喂养和体重的影响是否影响来自非特异性副作用，例如恶心。当前的应用程序通过评估胃肠道（GI）的肌电变化，作为VNS喂养影响的潜在机制行为，同时比较这些反应对催吐激活的反应。我们计划通过使用雪貂来实现这一目标模型，这是研究雌性，迷走神经和GI生理学的金标准。我们将测试假设迷走神经的电刺激可以减少食物摄入而无需触发恶心的指标，例如破坏的GI肌电反应，退缩和呕吐。我们将完成三个目标。 AIM 1：使用在进食行为期间定义个性化的GI肌电图案机器学习分类。动物将用附着在胃肠道浆液的平面电极植入从胃底近端到远端十二指肠的表面。我们将使用机器学习对GI进行分类与散发相关的状态相比胃内雌激素和高振幅和频率VN已知会触发呕吐。目标2：测试功效腹部VN的减少餐大小，而不会触发GI肌电反应的干扰，退缩，和呕吐。将使用多种刺激参数对动物进行腹部VN的影响。进食行为和多站点GI肌电记录。目标3：确定宫颈VN的功效控制餐大小而不会产生脱靶效应（胃肠道肌电反应的中断，退缩，呕吐，心率变化或血压）。我们将测试宫颈VNS参数对喂养行为，胃肠道肌电反应，退缩，呕吐，听到率变异性和血压。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 在清醒自由移动的动物中的肌电反应模式，用于比较治疗和脱靶效果 VN关于喂养，胃肠道活动，雌激素和心血管功能的VN。这项计划的研究很重要因为VNS疗法可能会为高肥胖症患者提供一线治疗选择对行为或药理治疗的难治性，与其他手术减轻手术干预不同，例如胃搭桥设备关闭或完全拆卸。