Metabolomic predictors of insulin resistance and diabetes

胰岛素抵抗和糖尿病的代谢组预测因子

• 批准号: 8109835
• 负责人: ROBERT E GERSZTEN
• 金额: $ 63.66万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8109835
• 关键词: Behavioral; Bile Acids; Biochemistry; Bioinformatics; Biological; Biological Markers; Biology; Blood Circulation; Body mass index; Cells; Clinical; Clinical Data; Comb animal structure; Data; Derivation procedure; Development; Diabetes Mellitus; Diagnosis; Disease; Disease Marker; Epidemiology; Framingham Heart Study; Future; Genetic; Genetic Determinism; Genetic Polymorphism; Glucose; Goals; Health; Human; Human Genome; Hyperglycemia; Individual; Ingestion; Institutes; Insulin; Insulin Resistance; Interdisciplinary Study; Investigation; Knowledge; Laboratory Markers; Link; Mass Spectrum Analysis; Metabolic; Metabolic Diseases; Metabolic Pathway; Monitor; Noise; Non-Insulin-Dependent Diabetes Mellitus; OGTT; Other Genetics; Pathway interactions; Phenotype; Physiology; Pilot Projects; Plant Roots; Plasma; Population; Population Genetics; Prevention; Process; Recording of previous events; Relative (related person); Research; Research Personnel; Resources; Rest; Risk; Risk Factors; Risk Marker; Sampling; Study Subject; Surveys; Technology; Time; Validation; Variant; base; clinically relevant; cohort; fasting glucose; follow-up; genetic association; genetic variant; genome wide association study; human disease; impaired glucose tolerance; liquid chromatography mass spectrometry; metabolomics; novel; novel marker; nutrition; population based; pre-clinical; prospective; response; small molecule; trait

DESCRIPTION (provided by applicant): Metabolic diseases present particular difficulty for clinicians because they are often present for years before becoming clinically apparent. Clinical risk predictors of future diabetes mellitus (DM) are imperfect. A robust set of predictors of at risk individuals is of particular importance because the delay or prevention of type 2 DM may be possible via both behavioral and pharmacological approaches. One new avenue for the identification of novel disease markers is being opened by the global analysis of the human metabolome. An emerging set of technologies, based on mass spectrometry, enables the monitoring of hundreds of metabolites from biological samples. A second new avenue for the identification of novel risk factors is afforded by genome- wide association studies (GWAS), which have begun to yield robust, reproducible genetic associations with type 2 DM. Many of these genetic variants have occurred in heretofore unsuspected pathways. Indeed, many variants are not correlated with intermediate glycemic traits such as fasting glucose or insulin. Thus, there is an urgent need to characterize the metabolic consequences of these newly discovered genetic variants, and to identify additional genetic variants that are more closely related to pathogenic metabolic signatures. We postulate that combining metabolomic, genetic, and clinical data in human populations will provide a rich opportunity to identify metabolite signatures of those destined to develop overt DM. To achieve this goal, we will leverage the unique resources of the Framingham Heart Study (FHS), a well- characterized, prospective cohort in which GWAS and comprehensive metabolomic profiling are possible. In Specific Aim 1, we will document changes in plasma metabolite levels with glucose loading in individuals with and without insulin resistance. Building upon preliminary studies already performed, we will profile 500 plasma metabolites in samples obtained from ~3100 FHS subjects before and after an oral glucose tolerance test (OGTT). We will then assess the relation of two phenotypes, insulin resistance and impaired glucose tolerance, with the change in plasma metabolite levels in response to OGTT. In Specific Aim 2, we will determine whether changes in plasma metabolite concentrations with glucose loading predict the development of insulin resistance and diabetes. We will use multivariable regression to examine the relation between plasma metabolites and 2 endpoints: incident DM and insulin resistance. In Specific Aim 3, we will characterize the genetic determinants of metabolites associated with insulin resistance and diabetes. We will (A) analyze the relation of metabolites identified in Aims 1 and 2 with common genetic variants using GWAS, and (B) characterize differences in plasma metabolite concentrations in individuals with and without validated genetic polymorphisms associated with DM. Thus, our goal is to identify novel markers of preclinical disease and illuminate pathways contributing to DM. PUBLIC HEALTH RELEVANCE: Current treatments for diabetes (DM) are only partially successful, in part because they are based on limited knowledge of its root causes. Furthermore, there is no way to accurately predict who will develop DM, thus limiting our ability to intervene effectively. Our goal is to use genetics and other systematic approaches to develop novel markers of preclinical disease and illuminate our understanding of the underlying disease mechanisms in DM.

描述（由申请人提供）：代谢性疾病对临床医生来说特别困难，因为它们通常在临床上变得明显之前存在多年。未来糖尿病（DM）的临床风险预测是不完善的。一组强有力的高危个体预测因素尤其重要，因为通过行为和药理方法可以延迟或预防2型糖尿病。通过对人类代谢组的全球分析，正在开辟一条新的途径来鉴定新的疾病标志物。一套新兴的技术，基于质谱法，能够监测数百种代谢物的生物样品。全基因组关联研究（GWAS）提供了鉴定新风险因素的第二个新途径，其已经开始产生与2型DM的稳健的、可再现的遗传关联。这些遗传变异中的许多发生在迄今为止未被怀疑的途径中。事实上，许多变异与中间血糖性状（如空腹血糖或胰岛素）无关。因此，迫切需要表征这些新发现的遗传变异的代谢后果，并鉴定与致病代谢特征更密切相关的其他遗传变异。我们假设，结合人群中的代谢组学、遗传学和临床数据，将为确定那些注定发展为显性DM的代谢物特征提供丰富的机会。为了实现这一目标，我们将利用FHS的独特资源，FHS是一个特征良好的前瞻性队列，其中GWAS和全面的代谢组学分析是可能的。在具体目标1中，我们将记录有和无胰岛素抵抗个体的血浆代谢物水平随葡萄糖负荷的变化。在已经进行的初步研究的基础上，我们将在口服葡萄糖耐量试验（OGTT）前后从约3100名FHS受试者中获得的样本中分析500种血浆代谢物。然后，我们将评估两种表型，胰岛素抵抗和糖耐量受损，与OGTT后血浆代谢物水平变化的关系。在具体目标2中，我们将确定血浆代谢物浓度随葡萄糖负荷的变化是否可预测胰岛素抵抗和糖尿病的发生。我们将使用多变量回归来检查血浆代谢物与2个终点之间的关系：糖尿病事件和胰岛素抵抗。在具体目标3中，我们将描述与胰岛素抵抗和糖尿病相关的代谢物的遗传决定因素。我们将（A）使用GWAS分析目标1和2中鉴定的代谢物与常见遗传变异的关系，（B）描述具有和不具有经验证的DM相关遗传多态性的个体中血浆代谢物浓度的差异。因此，我们的目标是确定临床前疾病的新标志物，并阐明导致DM的途径。公共卫生相关性：目前糖尿病（DM）的治疗方法仅部分成功，部分原因是它们基于对其根本原因的有限了解。此外，没有办法准确预测谁会患上糖尿病，从而限制了我们有效干预的能力。我们的目标是利用遗传学和其他系统的方法来开发新的临床前疾病的标志物，并阐明我们对DM潜在疾病机制的理解。