Metabolomic predictors of insulin resistance and diabetes

项目摘要/摘要 在之前的资助期内，我们已经确定并验证了那些目标为 开发显性的T2D。这些代谢物在T2D发病前12年内升高 最初是葡萄糖耐量；改善了对超越临床危险因素的T2D的预测，并建立了 生化标记；并已被其他小组验证。我们现在已经把我们的研究扩展到 杰克逊心脏研究(JHS)的参与者，这是一个非裔美国人(AA)人口，有很高的 T2D及其并发症。我们还在一项关键的临床试验中测试了代谢物的预测价值， 糖尿病预防计划(DPP)。 我们的续展将利用在该奖项的前12年取得的关键进展。超越被命名的 我们与T2D事件有关的代谢物，我们最近对T2D和T2D的“整体代谢组”分析 JHS的相关特征已经提名了数百种未知化合物，这些化合物与现有的 用于明确识别的生化标记。我们将使用新颖的内部质谱学(MS)和 明确识别这些化合物的生物信息学工具。为了补充MS的工作，全基因组 代谢物和蛋白质的关联性研究和遗传相关分析将用于 将代谢物峰分配给途径(例如，基于与已知代谢物或与酶或 溶质携带者)，告知他们的身份。最后，代谢物相关遗传变异的精细定位，共- 已建立的T2D相关变异的本地化研究和孟德尔随机化实验将 用于评估T2D代谢物相关变异的因果关系。出现的推定因果变异 这些分析将在模型系统中得到验证，使用在我们的 实验室以及新的方法。 在特定的目标1中，我们将建立与T2D和T2D相关的未知代谢物峰的身份 相关性状，使用最先进的质谱学，并由GWA和遗传相关性提供信息 分析。在具体目标2中，我们将完善与T2D和T2D相关的代谢物的遗传结构 多民族队列中的相关特征(空腹血糖、胰岛素、血脂、糖化血红蛋白和HOMA-IR)，并测试是否 可能的因果变异也是T2D和这些相同特征的风险变异。在具体目标3中，我们将在功能上 检查模型系统中与代谢物相关的关键变量。我们将同时使用增益函数和损耗函数 重述人类代谢物图谱并测试对关键代谢功能影响的方法 代谢活跃的组织(如胰腺)中的胰岛素释放。最初的研究将集中在小说T2D上 生物标记物Acy-1，一种在肝脏表达最高的循环酶，它能裂解内源性N- 乙酰化氨基酸转变为自由循环形式。来自该多机构协作的所有数据都将 向整个科学界实时公开提供。

Project Summary/Abstract During prior funding periods we have identified and validated novel metabolite profiles of those destined to develop overt T2D. These metabolites were elevated up to 12 years before the onset of T2D in individuals who were initially glucose-tolerant; improved prediction of T2D beyond clinical risk factors and established biochemical markers; and have been validated by other groups. We have now extended our studies to participants in the Jackson Heart Study (JHS), an African American (AA) population with a high prevalence of T2D and its complications. We have also tested the predictive value of metabolites in a key clinical trial, the Diabetes Prevention Program (DPP). Our renewal will leverage critical advances made during the first 12 years of this award. Beyond the named metabolites that we have associated with incident T2D, our recent “whole metabolome” analyses of T2D and related traits in JHS have nominated hundreds of unknown compounds that are uncorrelated with existing biochemical markers for unambiguous identification. We will use novel, in-house mass spectrometry (MS) and bioinformatics tools to unambiguously identify these compounds. To complement the MS work, genome wide association studies (GWAS) and genetic correlation analyses of metabolites and proteins will be used to assign metabolite peaks to pathways (e.g., based on association with known metabolites or with enzymes or solute carriers) that inform their identity. Finally, fine mapping of metabolite-associated genetic variants, co- localization studies with established T2D-associated variants, and Mendelian Randomization experiments will be used to assess causality of metabolite-associated variants for T2D. Putative causal variants that emerge from these analyses will be validated in model systems, using techniques that are well established in our laboratory as well as novel approaches. In Specific Aim 1, we will establish the identity of unknown metabolite peaks that are associated with T2D and related traits, using state-of-the-art mass spectrometry, and informed by GWAS and genetic correlation analyses. In Specific Aim 2, we will refine the genetic architecture of metabolites associated with T2D and related traits (fasting glucose, insulin, lipids, HbA1c, and HOMA-IR) in multi-ethnic cohorts and test whether the likely causal variants are also risk variants for T2D and these same traits. In Specific Aim 3, we will functionally examine key metabolite-related variants in model systems. We will use both gain- and loss-of-function approaches to recapitulate metabolite profiles seen in humans and test for effects on key metabolic functions (e.g., insulin release) in metabolically active tissues (e.g., pancreas). Initial studies will focus on the novel T2D biomarker, ACY-1, a circulating enzyme most highly expressed by the liver which cleaves endogenous N- acetylated amino acids to their free circulating forms. All data from this multi-institutional collaboration will be made publicly available in real time for the entire scientific community.