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

• 批准号: 9796542
• 负责人: Charles Christopher Horn
• 金额: $ 48.86万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9796542
• 关键词: Abdomen; Address; Adult; Adverse effects; Affect; Anatomy; Animals; Behavioral; Blood Pressure; Body Weight; Body Weight decreased; Cardiovascular Diseases; Cardiovascular Physiology; Cardiovascular system; Cervical; Chemicals; Chronic; Classification; Comorbidity; Consumption; Data; Devices; Diabetes Mellitus; Distal; Duodenum; Eating; Effectiveness; 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; design; effective therapy; efficacy testing; experimental study; feeding; gastric fundus; gastrointestinal; gastrointestinal function; healthy weight; heart rate variability; indexing; innovation; insight; machine learning algorithm; obesity treatment; personalized medicine; pre-clinical; predicting response; recruit; reduced food intake; response; side effect; therapeutic target; vagus nerve stimulation; weight loss intervention

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.

肥胖影响了近40%的美国成年人，并与高水平的合并症有关，包括 癌症、心血管疾病和糖尿病。虽然有效的治疗，最小的副作用， 缺乏迷走神经刺激（VNS）可以减轻体重并抑制摄食行为。几乎没有 然而，对其机制的深入了解，以及VNS是否对摄食和体重产生影响尚不清楚 非特异性的副作用比如恶心本申请通过以下方式直接解决这些问题： 评估胃肠道（GI）肌电变化作为VNS影响进食的潜在机制 行为，同时将这些反应与催吐激活进行比较。我们计划用雪貂 模型，这是研究呕吐、迷走神经和GI生理学的金标准。我们将测试 迷走神经的电刺激可以减少食物摄入而不触发 恶心的指标，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。目的1：定义进食行为期间的个性化GI肌电模式， 机器学习分类将在动物体内植入附着在GI serectin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 与呕吐相关状态相比，进餐的肌电模式，包括由 胃内呕吐和高振幅和频率VNS已知触发呕吐。目的2：检验疗效 腹部VNS减少进餐量而不触发GI肌电反应中断，干呕， 还有呕吐将使用各种刺激参数评估动物腹部VNS的影响， 进食行为和多部位GI肌电记录。目的3：确定颈部迷走神经刺激在 控制膳食量而不产生脱靶效应（GI肌电反应的中断，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、GI肌电反应、干呕、呕吐、心率变异性和血压。我们 这种方法是创新的，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，用于比较治疗和脱靶效应 VNS对进食、胃肠道活动、呕吐和心血管功能的影响。这项计划中的研究意义重大 因为迷走神经刺激疗法可能为高度肥胖的患者提供一线治疗选择 行为或药物治疗难治，与其他减肥手术不同， 例如胃旁路，通过改变刺激参数、切换 器械关闭或完全取出。