Novel Metabolic Predictors of Diabetes in American Indians

项目摘要 美国印第安人(AIS)患有不成比例的2型糖尿病(T2D)。新型机械装置的发现 生物标志物是识别高危个体和制定针对此而制定有效预防策略的关键。 高危人群。作为对PA-12-165的响应，该项目利用收集到的独特资源的财富 根据强心研究(SHS)，对美国印第安人进行的最大规模纵向队列研究 年，以确定敏感和特定的代谢标记物，在临床前阶段预测T2D风险 超过和超过标准的临床因素包括肥胖、空腹血糖和胰岛素抵抗。 代谢组学是一项新兴的技术，可以同时识别和准确量化数百到 生物体液中有数千种代谢物。几种代谢物，如支链氨基酸、酰肉碱和脂类， 与T2D有关，但这些结果主要来自于几乎 完全是欧洲高加索人。然而，考虑到新陈代谢的遗传调节，确定了代谢物 在高加索人中，可能不会推广到可能具有不同基因构成的人工智能。此外，交叉- 截面分析不能捕捉代谢随时间变化的动态轨迹。此外，大多数 现有的研究在单一平台上测量了一系列预先选择的代谢物，但考虑到 人体代谢组和代谢物的种类繁多，没有一个单一的分析平台可以检测到所有 生物样品中的代谢物。我们假设血浆代谢物的纵向变化可以预测 与空腹血糖、胰岛素抵抗(IR)和肥胖无关T2D风险，以及T2D的代谢特征 人工智能与高加索人相似，但又不同。我们的目标是确定新的和敏感的T2D 超越经典T2D指标的特定于AIS的预测指标。为了实现这一目标，我们将反复 测量超过500种代谢物的浓度，包括支链氨基酸、碳水化合物、羟基酸、脂类、 以及肠道微生物衍生的代谢物，在空腹血浆中(相隔约5年)与正常血糖的SHS 参与者对&gt；进行了15年跟踪调查。假定的代谢物将在独立的纵向样本中复制 对人工智能进行了长达10年的跟踪。为了扩大覆盖面，我们将量化三种代谢物的浓度 互为补充的平台。每一次化验都将作为“靶向”和“非靶向”双重分析进行 提供假设驱动的定量数据和发现驱动的未识别的半定量数据 代谢物。未知化合物将通过建立良好的工作流程进行识别。多变量分析将是 旨在确定高于和超过标准临床因素的新的T2D预测因子。我们的多学科 团队由在糖尿病流行病学、代谢组学、分析领域具有互补专长的专家组成 化学、统计学和生物信息学。这项研究的结果将极大地促进我们对T2D的理解 病理，并有望减少或消除人工智能的T2D差异，这是一个重要的伦理问题，但 传统上对少数群体的研究不足，他们患有令人震惊的T2D和肥胖率。

Project Summary American Indians (AIs) suffer disproportionately from type 2 diabetes (T2D). Discovery of novel mechanistic biomarkers is the key to identify at-risk individuals and to develop effective preventive strategies tailored to this high risk population. In response to PA-12-165, this project leverages the wealth of unique resources collected by the Strong Heart Study (SHS), the largest longitudinal cohort study of American Indians followed over 25 years, to identify sensitive and specific metabolic markers that are predictive of T2D risk at preclinical stages above and over standard clinical factors including obesity, fasting glucose and insulin resistance. Metabolomics is an emerging technology that can simultaneously identify and accurately quantify hundreds to thousands of metabolites in biofluids. Several metabolites, such as BCAAs, acylcarnitines, and lipids, have been associated with T2D, but these results were largely derived from cross-sectional studies in almost exclusively European Caucasians. However, given the genetic regulation of metabolism, metabolites identified in Caucasians may not be generalized to AIs who may have a different genetic make-up. In addition, cross- sectional analysis cannot capture the dynamic trajectory of metabolic changes over time. Moreover, most existing studies measured a list of pre-selected metabolites on a single platform, but given the complexity of the human metabolome and the substantial diversity of metabolites, no single analytical platform can detect all metabolites in a biological sample. We hypothesize that longitudinal changes in plasma metabolites predict T2D risk independent of fasting glucose, insulin resistance (IR) and obesity, and that metabolic profiles of T2D in AIs are similar to, but distinct from, those in Caucasians. Our goal here is to identify novel and sensitive T2D predictors that are specific to AIs beyond classical T2D indicators. To achieve this, we will repeatedly measure concentrations of over 500 metabolites, including BCAAs, carbohydrates, hydroxyl acids, lipids, as well as gut microbial-derived metabolites, in fasting plasma (~5 yr apart) from normoglycemic SHS participants followed >15 years. Putative metabolites will be replicated in an independent longitudinal sample of AIs followed for 10 years. To increase coverage, we will quantify metabolites concentrations on three complementary platforms. Each assay will be performed as a dual 'targeted' and 'untargeted' analyses to provide both hypothesis-driven quantitative data and discovery-driven semi-quantitative data of unidentified metabolites. Unknown compounds will be identified by well-established workflows. Multivariate analyses will be conducted to identify novel T2D predictors above and over standard clinical factors. Our multidisciplinary team consists of experts with complementary expertise in diabetes epidemiology, metabolomics, analytical chemistry, statistics and bioinformatics. Findings of this study will greatly advance our understanding of T2D pathology, and hold promise for reducing or eliminating T2D disparity in AIs, an ethnically important but traditionally understudied minority group suffering from alarmingly high rates of T2D and obesity.