Novel Metabolic Predictors of Diabetes in American Indians

项目摘要 美国印第安人（AI）不成比例地患有2型糖尿病（T2 D）。新机制的发现 生物标志物是识别高危个体和制定有效预防策略的关键。 高危人群。作为对PA-12-165的回应，该项目利用了收集到的丰富的独特资源， 由强大的心脏研究（SHS），最大的纵向队列研究的美洲印第安人超过25 年，以确定在临床前阶段预测T2 D风险的敏感和特异性代谢标志物 超过标准的临床因素包括肥胖、空腹血糖和胰岛素抵抗。 代谢组学是一种新兴技术，可以同时识别和准确定量数百种代谢物， 生物体液中的数千种代谢物几种代谢物，如支链氨基酸，酰基肉毒碱和脂质， 与T2 D相关，但这些结果主要来自几乎所有的横断面研究。 只有欧洲高加索人。然而，考虑到代谢的遗传调节， 在高加索人中，可能不会推广到可能具有不同遗传组成的AI。此外，跨- 分段分析不能捕获代谢变化随时间的动态轨迹。而且大部分 现有的研究在一个平台上测量了一系列预先选择的代谢物，但考虑到 人体代谢物组和代谢物的多样性，没有一个单一的分析平台可以检测所有的 生物样品中的代谢物。我们假设血浆代谢物的纵向变化预示着 2型糖尿病风险独立于空腹血糖、胰岛素抵抗（IR）和肥胖， 与高加索人相似，但又不同。我们的目标是识别新的和敏感的T2 D 这些预测因子是AI特有的，超出了传统的T2 D指标。为了实现这一目标，我们将不断 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，脂质， 血糖正常的SHS患者空腹血浆（相隔约5年）中的肠道微生物衍生代谢物 参与者随访>15年。将在独立的纵向样本中复制推定代谢物 10年的人工智能研究。为了增加覆盖面，我们将量化代谢物浓度在三个 互补平台。每项试验将作为双重“靶向”和“非靶向”分析进行， 提供假设驱动的定量数据和发现驱动的半定量数据， 代谢物。将通过完善的工作流程鉴别未知化合物。将进行多变量分析 旨在确定高于标准临床因素的新型T2 D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 化学、统计学和生物信息学。这项研究的发现将大大促进我们对T2 D的理解 病理学，并有望减少或消除AI中的T2 D差异，这是一个重要的种族，但 传统上未被充分研究的少数群体患有高得惊人的T2 D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.