Adipose Tissue-Derived Extracellular Vesicles as Early Biomarkers of Type 2 Diabetes

脂肪组织来源的细胞外囊泡作为 2 型糖尿病的早期生物标志物

• 批准号: 10504814
• 负责人: Jia Fan
• 金额: $ 25.85万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-10 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10504814
• 关键词: Adipocytes; Adipose tissue; Affect; Age; Appearance; Attenuated; Binding; Biological Assay; Biological Factors; Biological Markers; Blood Circulation; Body mass index; Cardiometabolic Disease; Cardiovascular Diseases; Cardiovascular system; Cells; Centers of Research Excellence; Chronic Disease; Clinical; Clinical Data; Clinical Research; Cohort Studies; Collaborations; Detection; Development; Diabetes prevention; Disease; Disease Progression; Distant; Early Diagnosis; Early identification; Enrollment; Evaluation; Event; Exhibits; Follow-Up Studies; Functional disorder; Glucose; Glucose Intolerance; Glycosylated hemoglobin A; Heart; Human; Individual; Inflammatory Response; Innate Immune Response; Insulin Resistance; Investigation; Label; Lead; Louisiana; Mass Spectrum Analysis; Measurement; Measures; Mediating; Membrane; Mentors; Mentorship; Metabolic Diseases; Metabolic syndrome; Methods; Modeling; Morbidity - disease rate; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Organ; Participant; Pathologic; Pathway interactions; Pattern; Peptide Hydrolases; Performance; Plasma; Prediabetes syndrome; Procedures; Process; Proteins; Proteomics; Race; Reporting; Reproducibility; Research; Research Personnel; Resources; Risk; Sampling; Serum; Signal Transduction; Standardization; Statistical Models; Symptoms; Therapeutic Intervention; Tissues; Translating; Translational Research; Translations; adipokines; base; biomarker development; candidate marker; clinical application; clinical practice; clinical translation; cost; detection limit; diabetes risk; differential expression; early detection biomarkers; extracellular vesicles; glucose metabolism; high risk; improved; innovation; machine learning algorithm; machine learning model; macrophage; mortality; nanoGold; nanoparticle; nanoplasmonic; novel; obese patients; obese person; obesity development; obesity risk; potential biomarker; predictive marker; prevent; programs; protein biomarkers; response; risk prediction; sex; supervised learning; targeted biomarker; vesicular release

SUMMARY Obesity and type 2 diabetes (T2D) are interrelated pathological conditions frequently observed to lead to cardiovascular complications. Obesity-associated T2D is among the most-costly chronic diseases in the US, and a major cause of morbidity and mortality. There is a substantial latency period preceding T2D development, and many nascent T2D cases can be prevented if they are identified prior to tissue damage. Until now, there is still a lack of reliable clinical assay to identify high-risk individuals before the appearance of T2D clinical symptoms. Adipose tissue dysfunction is a central driver of T2D and adipose tissue is recently reported to produce extracellular vesicles (EVs), which can enter into circulation and influence whole-body glucose metabolism and contribute to the development of insulin resistance. Adipose tissue EVs released during disease progression represent excellent biomarker candidates for T2D risk prediction compared to traditional soluble adipokines, since adipose tissue EVs contain bio-active cargoes that act on target organs to promote insulin resistance and ultimately T2D. Adipose tissue EVs are also likely to be more stable in plasma than traditional soluble protein biomarkers that may be more likely to be degraded by serum protease activity. The assay that could specifically capture and quantify these adipose tissue EVs associated with T2D may, therefore, serve to early identify obese individuals at high risk for T2D before they exhibit any clinical symptoms. The overall objective of this study is to discover and validate novel EVs-based biomarkers for identifying obese individuals at high risk for the development of pre-T2D and T2D. In Aim 1, we propose to employ a quantitative proteomic labeling method to identify EV proteins specifically, or predominantly present in adipose tissue EVs isolated from obese patients with T2D, preT2D, or without T2D. These EV markers could serve as targets for probes to specifically detect T2D or preT2D-associated EV levels. To promote translation of EVs into clinical practice, we will utilize Aim 2 to adapt our innovative Nanoplasmon-enhanced scattering assay to quantify specific adipose tissue EVs associated with increased T2D risk using probes for EV markers and establish optimal conditions to detect T2D or preT2D-associated adipose tissue EVs, standardize the assay procedure, and evaluate the analytical performance of the assay. To more effectively translate adipose tissue EV markers into clinical application, we will validate the Nanoplasmonic assay for adipose tissue EV detection in plasma to permit early detection of increased T2D risk to guide therapeutic interventions intended to attenuate disease progression prior to tissue damage. To this end, Aim 3 proposes to employ the optimized Nanoplasmonic assay to evaluate adipose tissue EVs on T2D prediction in an obese population from the long-term Bogalusa Heart Study cohort, and establish a machine learning model to assess the EV markers both individually and in combination over and above a set of known factors for T2D prediction. I am confident to successfully complete the proposed Aims and develop into an independent investigator with the support of the COBRE program and guidance from the mentor team.

总结 肥胖和2型糖尿病（T2 D）是经常观察到的相互关联的病理状况， 心血管并发症肥胖相关的T2 D是美国最昂贵的慢性疾病之一， 发病率和死亡率的主要原因。在T2 D发展之前有相当长的潜伏期， 如果在组织损伤之前识别出许多新生T2 D病例，则可以预防它们。直到现在， 缺乏可靠的临床分析，以确定T2 D临床症状出现之前的高风险个体。 脂肪组织功能障碍是T2 D的主要驱动因素，最近报道脂肪组织产生 细胞外囊泡（EV），可进入循环并影响全身葡萄糖代谢， 导致胰岛素抵抗的发展。疾病进展期间释放的脂肪组织EV 与传统的可溶性脂肪因子相比， 由于脂肪组织EV含有作用于靶器官以促进胰岛素抗性的生物活性物质， 最后是T2 D。脂肪组织EV在血浆中也可能比传统的可溶性蛋白质更稳定 这些生物标志物可能更可能被血清蛋白酶活性降解。这种检测方法可以特异性地 因此，捕获和量化这些与T2 D相关的脂肪组织EV可以用于早期识别肥胖 在出现任何临床症状之前，T2 D的高风险个体。本研究的总体目标是 发现和验证新的基于EV的生物标志物，用于识别肥胖个体的高风险， Pre-T2 D和T2 D的发展在目标1中，我们提出采用定量蛋白质组标记方法， 特异性或主要存在于从肥胖患者分离的脂肪组织EV中的EV蛋白质的鉴定 有T2 D、PreT 2D或无T2 D。这些EV标志物可以作为探针的靶标， T2 D或PreT 2D相关EV水平。为了促进电动汽车转化为临床实践，我们将利用目标2， 调整我们创新的Nanoplasmon增强散射测定，以量化特定的脂肪组织EV 与使用EV标志物探针增加的T2 D风险相关，并建立检测T2 D的最佳条件 或preT 2D相关脂肪组织EV，标准化测定程序，并评估分析结果。 测定的性能。为了更有效地将脂肪组织EV标志物转化为临床应用，我们 将验证用于血浆中脂肪组织EV检测的Nanoplasmonic测定，以允许早期检测 T2 D风险增加，以指导旨在在组织 损害为此，目标3提出采用优化的纳米等离子体测定来评估脂肪组织 在长期博加卢萨心脏研究队列的肥胖人群中，EV对T2 D的预测，并建立 机器学习模型，以单独地和组合地评估EV标志物， T2 D预测的已知因素。我有信心成功地完成拟议的目标，并发展成为 在COBRE计划的支持和导师团队的指导下，独立调查员。