Transition to a Western diet and cardiometabolic risk: biomarkers derived from the microbiome

向西方饮食的转变和心脏代谢风险：源自微生物组的生物标志物

基本信息

批准号：
9539999
负责人：
PENNY GORDON-LARSEN
金额：
$ 61.29万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-17 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9539999
关键词：
Adult Age Animal Sources Animals Biological Markers Blood Body mass index Cardiovascular Diseases China Chinese People Communities Complex Consent Country Data Development Diabetes Mellitus Diet Dietary Component Dietary History Disease Disease Outcome Disease Pathway Environment Epidemiologic Factors Food Funding Fusobacteria Geographic Locations Geography Health Health Surveys Heart Diseases Income Individual Individual Differences Inflammation Inflammatory Insulin Resistance Low income Macronutrients Nutrition Malignant Neoplasms Mediating Metabolic Diseases Metabolism Modeling Modernization Non-Insulin-Dependent Diabetes Mellitus Nutrition Surveys Obesity Oils Outcome Participant Pattern Physical activity Plasma Population Population Study Proteobacteria Province Regression Analysis Research Risk Role Sampling Series Statistical Models System Time United States National Institutes of Health Urbanization Validation Variant Weight Work aged base cardiometabolic risk cardiometabolism cohort disorder risk epidemiologic data experience fecal microbiome follow-up gut microbiota high dimensionality improved insight lifestyle factors metabolome metabolomics microbial microbial community microbial host microbiome microbiota microbiota metabolites novel predictive marker predictive modeling public health relevance rRNA Genes repository response socioeconomics western diet

项目摘要

DESCRIPTION (provided by applicant): Despite recent emergence of gut microbiota as a novel factor influencing obesity and related metabolic disorders, such as diabetes and cardiovascular disease, very little is known about the role of long-term diet on microbially mediated disease risk. The primary reasons for this lack of understanding are: 1) lack of high quality, long-term diet data in combination with microbiome data; 2) lack of data in populations undergoing rapid changes in environments and outcomes, with enough variability to create a range of exposures; and 3) insufficient use of complex statistical models to allow examination of each piece of the time-dependent, complex system. The China Health and Nutrition Survey (CHNS), an NIH-funded study of more than 15,000 individuals followed over 25 years, provides high quality longitudinal data and captures the transition from traditional to Western diet in parallel with urbanization and emergence of obesity and cardiovascular disease during the past two decades. We will use these data to generate insights that would not be obtainable in studies with subjects who consume only a Westernized diet. Using sophisticated statistical models we propose to examine whether information on gut microbiota composition along with data on the plasma metabolome can predict markers of cardiometabolic disease (body mass index, central adiposity, diabetes and inflammation), allowing us to implicate microbiota in disease pathways. We propose to collect fecal samples from 10,000 adult CHNS participants aged 30-65 at the 2015 exam. In a subsample from two neighboring Southern provinces (Guizhou, actively urbanizing, n=500; and Hunan, already urbanized, n=500) varying in current (and long-term change) in Western diet, we will create microbial sequences in the 16S V3-V4 region (n=1,000) and store the remaining (n=9,000) fecal samples. We will select a subsample with 16S data who have consumed a traditional diet over the 25 years (n=400) and conduct plasma metabolomics of the host. In the CHNS subsample with microbiota plus metabolome (n=400) data, we will collect replicate blood and fecal samples in CHNS2017 to derive 16S and plasma metabolomics data to assess two-year changes (2015-2017) in diet, gut microbiota, plasma metabolites and in markers of cardiometabolic disease. We will examine whether gut microbiota and plasma metabolites differ depending upon when, within the 25-year period, diet changes occur, and if they are associated with health outcomes. In our longitudinal subsample we will examine changes in markers of Western diet in relation to concurrent changes in microbial diversity and community composition, and metabolites. We first use a series of standard regression models and then high dimensional regression analysis with variable selection and validation to build predictive models. We capitalize upon an established and well-characterized, large cohort with far greater variability in diet and microbial communities than studies in cohorts on only Western diets. The proposed project will substantially transform current understanding of the intersection of diet, gut microbiota, host metabolism and cardiometabolic disease.

描述（由适用提供）：尽管肠道菌群最近出现作为一种新因素会影响客观性和相关代谢疾病，例如糖尿病和心血管疾病，但对于长期饮食在微生物介导的疾病风险中的作用知之甚少。缺乏理解的主要原因是：1）缺乏与微生物组数据结合使用的高质量长期饮食数据； 2）缺乏正在经历环境和结果快速变化的人群中的数据，具有足够的可变性来创造一系列暴露； 3）不充分使用复杂的统计模型来检查每个时间依赖的复杂系统。《中国健康与营养调查》（CHNS）是一项由NIH资助的研究，对超过25年的15,000多名个人进行了研究，可提供高质量的纵向数据，并捕获了过去二十年来与肥胖和心血管疾病的城市化和肥胖症和心血管疾病的出现并行的过渡。我们将使用这些数据来产生与仅消费西方饮食的受试者研究中无法获得的见解。使用复杂的统计模型，我们建议检查有关肠道菌群组成的信息以及血浆代谢组的数据是否可以预测心脏代谢疾病的标志物（体重指数，中枢性肥胖，糖尿病和炎症），从而使我们能够在疾病途径中实施菌群。我们建议在2015年考试中从10,000名成年CHN参与者那里收集粪便样本。在来自两个邻近的南部提供商（Guizhou，积极城市化，n = 500;和已经城市化的Hunan，n = 500）的子样本中，西方饮食中的当前（和长期变化）变化，我们将在16S V3-V4区域（n = 1,000）中创建微生物序列（n = 1,000），并存储剩余的（n = 9,000）fecal样品。我们将选择一个16S数据的子样本，这些数据在25年（n = 400）中消耗了传统饮食并进行宿主的血浆代谢组学。在使用Microbiota Plus代谢组（n = 400）数据的CHNS子样本中，我们将在CHNS2017中收集复制的血液和粪便样本，以得出16S和血浆代谢组学数据，以评估饮食中的两年变化（2015-2017），在饮食中，GUT Microbobiota，Cardasma Amerbolites和Cardiemers of Cardiomelials of Cardiomelity of Cardiomemolity of Cardiomememals of Cardiomemolity of Cardiomemolity。我们将研究肠道菌群和血浆代谢产物是否不同，具体取决于何时发生饮食的改变以及它们是否与健康结果相关。在我们的纵向子样本中，我们将研究西方饮食标记的变化，这些变化与微生物多样性和社区组成以及代谢产物的并发变化有关。我们首先使用一系列标准回归模型，然后使用具有可变选择和验证的高维回归分析来构建预测模型。我们利用了一个既定且特征良好的大型队列，其饮食和微生物群落的可变性要比仅在西方饮食方面的研究要大得多。拟议的项目将实质上改变对饮食，肠道菌群，宿主代谢和心脏代谢疾病的交点的当前理解。