Metabolomic predictors of insulin resistance and diabetes

项目概要/摘要 在之前的资助期间，我们已经确定并验证了那些注定要 发展为显性T2 D。这些代谢物在T2 D患者发病前12年升高， 最初对葡萄糖耐受;改善了T2 D的预测，超出了临床风险因素，并建立了 生化标记;并已被其他团体验证。我们现在已经将研究扩展到 杰克逊心脏研究（JHS）的参与者，一个非裔美国人（AA）人群， T2 D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）。 我们的续约将利用该奖项前12年取得的关键进展。超越命名 我们最近对T2 D的“全代谢组学”分析， JHS中的相关性状提名了数百种与现有的 生物化学标记物，用于明确识别。我们将使用新型的内部质谱法（MS）， 生物信息学工具来明确识别这些化合物。为了补充MS工作，全基因组 关联研究（GWAS）和代谢物和蛋白质的遗传相关性分析将用于 将代谢物峰分配给途径（例如，基于与已知代谢物或酶的结合， 溶质载体），其告知它们的身份。最后，代谢物相关遗传变异的精细作图， 使用已建立的T2 D相关变体进行定位研究，孟德尔随机化实验将 用于评估T2 D代谢物相关变体的因果关系。出现的假定的因果变异 从这些分析将在模型系统中得到验证，使用我们的 实验室和新方法。 在具体目标1中，我们将确定与T2 D相关的未知代谢物峰的鉴别， 相关性状，使用最先进的质谱法，并通过GWAS和遗传相关性获得信息 分析。在具体目标2中，我们将完善与T2 D相关的代谢物的遗传结构， 相关性状（空腹血糖、胰岛素、血脂、HbA 1c和HOMA-IR），并检测 可能的因果变异也是T2 D和这些相同性状的风险变异。在具体目标3中，我们将在功能上 检查模型系统中的关键代谢物相关变体。我们将同时使用增益函数和损失函数 概括人体代谢物谱并测试对关键代谢功能影响的方法 (e.g.,胰岛素释放）在代谢活性组织（例如，胰腺）。最初的研究将集中在新的T2 D 生物标志物，ACY-1，一种由肝脏最高表达的循环酶，其切割内源性N- 乙酰化氨基酸转化为它们的自由循环形式。来自多机构合作的所有数据将 在真实的时间内向整个科学界公开。

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.