Novel Metabolic Predictors of Diabetes in American Indians

美洲印第安人糖尿病的新代谢预测因子

• 批准号: 9176506
• 负责人: Oliver Fiehn
• 金额: $ 74.3万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9176506
• 关键词: Acids; American Indians; Amino Acids; Analytical Chemistry; Betaine; Bioinformatics; Biological; Biological Assay; Biological Markers; Branched-Chain Amino Acids; Carbohydrates; Carnitine; Caucasians; Ceramides; Choline; Clinical; Cohort Studies; Cross-Sectional Studies; Data; Data Discovery; Development; Diabetes Mellitus; Emerging Technologies; Epidemiology; Ethnic group; European; Fasting; Food; General Population; Genetic; Goals; Heart; Human; Hydroxyl Radical; Individual; Insulin Resistance; Intervention; Life Style; Link; Lipids; Measures; Mediating; Mediation; Mediator of activation protein; Metabolic; Metabolic Diseases; Metabolic Marker; Metabolic Pathway; Metabolism; Methods; Minority Groups; Multivariate Analysis; Native Americans; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Participant; Pathology; Pathway interactions; Phospholipids; Plasma; Population; Prevalence; Prevention; Prevention strategy; Prognostic Marker; Regulation; Resources; Risk; Risk Factors; Sampling; Sphingomyelins; Staging; Testing; Time; Triglycerides; Visit; Work; acylcarnitine; cohort; combat; design; diabetes risk; fasting glucose; follow-up; genetic makeup; high risk; interest; metabolic profile; metabolome; metabolomics; microbial; multidisciplinary; novel; pre-clinical; prognostic value; prospective; racial and ethnic; response; small molecule; specific biomarkers; statistics; trimethyloxamine

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.

项目概要 美洲印第安人 (AI) 患有 2 型糖尿病 (T2D) 的比例不成比例。发现新机制 生物标志物是识别高危个体并为此制定有效预防策略的关键 高危人群。为了响应 PA-12-165，该项目利用了收集到的丰富的独特资源 强心脏研究 (SHS) 是针对美洲印第安人的最大纵向队列研究，对超过 25 名 多年，以确定在临床前阶段预测 T2D 风险的敏感和特异代谢标志物 超出标准的临床因素，包括肥胖、空腹血糖和胰岛素抵抗。 代谢组学是一项新兴技术，可以同时识别和准确量化数百个 生物体液中存在数千种代谢物。多种代谢物，例如支链氨基酸、酰基肉碱和脂质，具有 与 T2D 相关，但这些结果主要来自几乎所有领域的横断面研究 仅限欧洲白种人。然而，考虑到代谢的遗传调控，代谢物被鉴定 白种人中的这种现象可能无法推广到具有不同基因组成的人工智能。此外，跨 分段分析无法捕捉代谢随时间变化的动态轨迹。而且，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，但考虑到其复杂性 人类代谢组和代谢物的巨大多样性，没有一个单一的分析平台可以检测所有 生物样品中的代谢物。我们假设血浆代谢物的纵向变化预测 T2D 风险与空腹血糖、胰岛素抵抗 (IR) 和肥胖无关，并且 T2D 的代谢特征 人工智能与白种人的相似，但又不同。我们的目标是识别新颖且敏感的 T2D 超越经典 T2D 指标的 AI 特有预测因子。为了实现这一目标，我们将反复 测量 500 多种代谢物的浓度，包括支链氨基酸、碳水化合物、羟基酸、脂质等 以及肠道微生物衍生的代谢物，存在于血糖正常的 SHS 的空腹血浆中（相隔约 5 年） 参与者随访超过 15 年。假定的代谢物将在独立的纵向样本中复制 的人工智能跟踪了 10 年。为了扩大覆盖范围，我们将量化三种代谢物的浓度 互补平台。每个测定将作为双重“靶向”和“非靶向”分析进行 提供未知的假设驱动的定量数据和发现驱动的半定量数据 代谢物。未知化合物将通过完善的工作流程进行识别。多变量分析将 旨在识别高于标准临床因素的新型 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、分析等方面具有互补专业知识的专家组成 化学、统计学和生物信息学。这项研究的结果将极大地增进我们对 T2D 的理解 病理学，并有望减少或消除人工智能中的 T2D 差异，这是一个在种族上很重要但 传统上研究不足的少数群体患 T2D 和肥胖率高得惊人。