Metabolomic predictors of insulin resistance and diabetes

描述（由申请人提供）：代谢性疾病对临床医生来说是特别困难的，因为它们通常在临床上表现出来之前存在多年。未来糖尿病（DM）的临床风险预测指标尚不完善。一套可靠的高危个体预测指标尤为重要，因为可以通过行为和药理学两种方法来延缓或预防2型糖尿病。人类代谢组的全球分析为鉴定新的疾病标志物开辟了一条新途径。一套基于质谱法的新兴技术能够监测生物样品中的数百种代谢物。鉴定新的危险因素的第二个新途径是全基因组关联研究（GWAS），该研究已经开始产生与2型糖尿病强有力的、可重复的遗传关联。许多这些遗传变异发生在迄今为止未被怀疑的途径中。事实上，许多变异与空腹血糖或胰岛素等中间血糖特征无关。因此，迫切需要表征这些新发现的遗传变异的代谢后果，并确定与致病代谢特征更密切相关的其他遗传变异。我们假设，结合人类人群的代谢组学、遗传和临床数据，将为识别那些注定要发展为显性糖尿病的人的代谢物特征提供丰富的机会。为了实现这一目标，我们将利用弗雷明汉心脏研究（FHS）的独特资源，这是一个特征明确的前瞻性队列，在该队列中，GWAS和全面的代谢组学分析是可能的。在特异性目标1中，我们将记录有和无胰岛素抵抗个体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，我们将对约3100名FHS受试者在口服葡萄糖耐量试验（OGTT）前后获得的样品中的500种血浆代谢物进行分析。然后，我们将评估胰岛素抵抗和糖耐量受损这两种表型与OGTT后血浆代谢物水平变化的关系。在Specific Aim 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能预测胰岛素抵抗和糖尿病的发展。我们将使用多变量回归来检验血浆代谢物与2个终点之间的关系：糖尿病和胰岛素抵抗。在特异性目标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和目标2中鉴定的代谢物与常见遗传变异的关系，(B)表征患有和未患有与糖尿病相关的经验证的遗传多态性的个体血浆代谢物浓度的差异。因此，我们的目标是鉴定临床前疾病的新标志物并阐明导致糖尿病的途径。目前对糖尿病的治疗只取得了部分成功，部分原因是这些治疗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发展为糖尿病，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临床前疾病的新标志物，并阐明我们对糖尿病潜在疾病机制的理解。

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.