Role of the Gut Microbiome in Reward Network Alterations in Obesity

肠道微生物组在肥胖奖励网络改变中的作用

• 批准号: 9895068
• 负责人: ARPANA GUPTA
• 金额: $ 11.7万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9895068
• 关键词: Address; Adjuvant Therapy; Affect; Agonist; Amino Acids; Aromatic Amino Acids; Behavior; Behavioral; Biologic Characteristic; Biological; Body Weight decreased; Brain; Branched-Chain Amino Acids; Calories; Clinical; Cognitive Therapy; Cross-Sectional Studies; Cues; Data; Desire for food; Development; Diet; Dietary Intervention; Eating; Eating Behavior; Effectiveness; Feces; Feeding behaviors; Female; Fiber; Firmicutes Bacteroidetes ratio; Food; Frequencies; Functional disorder; Funding; Future; Glutamates; Goals; Grant; Health; Homeostasis; Hunger; Immune; Immune system; Individual; Ingestion; Insulin Resistance; Intervention; Intervention Studies; Kynurenic Acid; Kynurenine; Lead; Life Style Modification; Link; Longitudinal Studies; Magnetic Resonance Imaging; Measures; Metagenomics; Modeling; N-Methylaspartate; Obesity; Obesity Epidemic; Outcome; Pathway interactions; Patients; Pattern; Peripheral; Pharmacological Treatment; Pharmacology; Phenotype; Play; Prediction of Response to Therapy; Prevalence; Prevention; Public Health; Quinolinic Acid; Recurrence; Reporting; Research; Rewards; Risk; Role; Sampling; Serum; Sex Differences; Shotguns; Signal Transduction; Source; Substance abuse problem; Systems Biology; Techniques; Testing; Therapeutic Intervention; Thinness; Treatment Efficacy; Treatment outcome; Tryptophan; Volatile Fatty Acids; Weight; Weight Gain; Woman; addiction; base; blood glucose regulation; clinical predictors; comorbidity; craving; emotion regulation; food addiction; food craving; gut microbiome; host-microbe interactions; male; medical complication; metabolomics; microbial; microbiome; microbiome alteration; neuroimaging; novel therapeutics; obesity treatment; patient subsets; pleasure; post intervention; predictive marker; primary outcome; psychologic; recruit; response; secondary outcome; somatosensory; weight maintenance

ABSTRACT The already funded K23 is focused on investigating the brain-immune interactions associated with maladaptive eating behaviors (disinhibited ingestive behaviors [DIB]). DIB is an important factor in the underlying pathophysiology observed in 25-37% of obese individuals where eating for pleasure overrides eating for homeostatic needs. This R03 application builds on the investigative efforts of the K23 by aiming to identify the role of altered brain gut microbiome (BGM) signaling contributing to DIB in the same obese sample recruited for the K23. I propose to address this goal both in a cross-sectional study, as well as in a longitudinal study determining changes in the BGM axis in response to two interventions: cognitive behavioral therapy (CBT) and a diet intervention (high fiber hypocaloric diet). While the cross-sectional study will identify alterations in BGM interactions between obese females with DIB compared to those without DIB, the longitudinal intervention studies aim to identify biomarkers that predict treatment responses and to identify biologically based subgroups of patients. The CBT intervention is aimed to strengthen prefrontal inhibitory influences on reward networks, thereby reversing maladaptive ingestive behaviors. The CBT intervention will be compared to a control diet intervention which will target the gut microbiome. The primary outcome is the normalization of DIB with the secondary outcome being weight loss. Recruitment will be restricted to females due to the known sex differences in DIB, with higher cravings, poorer weight loss and maintenance outcomes in obese females compared to obese males. The presence of DIB will be based on the Yale Food Addiction Scale (YFAS), a validated measure of disinhibited ingestion. Stool and serum to determine microbial-related measures (16sRNA sequencing, shotgun metagenomics, and metabolomics), and MRI to assess brain alterations in the extended reward network will be collected pre-and post-intervention and will be used to examine disinhibited ingestion-related differences and as predictors of clinical response. Advanced multivariate analytic techniques will be used to integrate data from multiple neuroimaging sources, microbiome and metabolite profiles, and behavioral data. This analysis will determine the unique variance associated with DIB in moderating the altered BGM axis at baseline and after the interventions. Integrating information from multiple central, peripheral, and behavioral sources will help increase the validity of the proposed BGM model and will identify phenotypes at increased risk for DIB and obesity. The proposed studies will focus on: 1) Identifying mechanisms underlying maladaptive eating behaviors in DIB, 2) identifying targets that can be modified by brain and gut targeted interventions, and 3) to generate pilot data for a larger mechanistic R01 proposal. Identifying biological characteristics of a subset of obese patients is essential for the development of more effective, personalized non-surgical treatments, which may be used in conjunction with pharmacological treatments.

摘要 已经获得资助的K23专注于研究与适应不良相关的脑免疫相互作用。 饮食行为（disinhibited ingestive behaviors [DIB]）DIB是一个重要因素， 在25-37%的肥胖个体中观察到的病理生理学， 自我平衡的需要此R 03应用程序建立在K23的调查工作的基础上，旨在确定 在招募的同一肥胖样本中，改变的脑肠道微生物组（BGM）信号传导对DIB的作用 对于K23。我建议在横向研究和纵向研究中解决这一目标 确定响应于两种干预的BGM轴的变化：认知行为疗法（CBT）和 饮食干预（高纤维低热量饮食）。虽然横断面研究将确定BGM的改变， 与没有DIB的肥胖女性相比，DIB肥胖女性之间的相互作用，纵向干预 研究旨在确定预测治疗反应的生物标志物，并确定基于生物学的亚组 病人。CBT干预旨在加强前额叶抑制对奖励网络的影响， 从而逆转不适应的摄食行为。CBT干预将与对照饮食进行比较 针对肠道微生物组的干预。主要结果是DIB的正常化， 次要结果是体重减轻。由于已知性别，招募将仅限于女性 DIB的差异，肥胖女性的渴望更高，体重减轻和维持结果较差 与肥胖男性相比。DIB的存在将基于耶鲁食物成瘾量表（YFAS）， 有效的抑制摄入措施。粪便和血清，以确定微生物相关措施 （16 sRNA测序，鸟枪宏基因组学和代谢组学），以及MRI来评估大脑中的改变。 扩展奖励网络将在干预前后收集，并将用于检查去抑制 摄食相关的差异，并作为临床反应的预测因子。高级多元分析技术 将用于整合来自多个神经成像来源、微生物组和代谢物谱的数据， 行为数据该分析将确定与DIB相关的独特方差， 基线和干预后的BGM轴。整合来自多个中央、外围和 行为来源将有助于提高提出的BGM模型的有效性，并将在 DIB和肥胖的风险增加。拟议的研究将集中在：1）确定潜在的机制 DIB中的适应不良饮食行为，2）确定可以通过大脑和肠道靶向改变的靶点 干预措施，以及3）为更大的机械R 01提案生成试验数据。识别生物 肥胖患者子集的特征对于开发更有效、个性化的 非手术治疗，其可以与药物治疗结合使用。