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: 10664054
• 负责人: Jia Fan
• 金额: $ 25.77万
• 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/10664054
• 关键词: Acceleration; Adipocytes; Adipose tissue; Affect; Age; Appearance; Attenuated; Binding; Biological Assay; Biological Factors; Biological Markers; Body mass index; Cardiometabolic Disease; Cardiovascular Diseases; Cardiovascular system; Cells; Centers of Research Excellence; Chronic Disease; Circulation; Clinical; Clinical Data; Clinical Research; Cohort Studies; Collaborations; Detection; Development; Diabetes prevention; Disease; Disease Progression; Distant; Early Diagnosis; Early identification; Enrollment; Evaluation; Event; Exhibits; Friends; Functional disorder; Glucose; Glucose Intolerance; Glycosylated hemoglobin A; Heart; Human; Individual; Inflammatory Response; Innate Immune Response; Insulin Resistance; Investigation; Label; Lead; Louisiana; Macrophage; 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; biomarker development; candidate marker; clinical application; clinical practice; clinical translation; cost; detection limit; diabetes risk; differential expression; early detection biomarkers; extracellular vesicles; follow-up; glucose metabolism; high risk; high risk population; improved; innovation; machine learning algorithm; machine learning model; mortality; nanoGold; nanoparticle; nanoplasmonic; novel; obese patients; obese person; obesity development; obesity risk; potential biomarker; predictive marker; prevent; programs; progression risk; protein biomarkers; response; risk prediction; sex; success; 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.

摘要 肥胖和2型糖尿病(T2D)是相互关联的病理状况，经常被观察到导致 心血管并发症。与肥胖相关的T2D是美国成本最高的慢性病之一， 这是导致发病率和死亡率的主要原因。在T2D开发之前有相当长的潜伏期，并且 许多新生的T2D病例如果在组织损伤之前被识别出来，是可以预防的。到目前为止，仍然有 在出现T2D临床症状之前，缺乏可靠的临床检测方法来识别高危个体。 脂肪组织功能障碍是T2D的主要驱动因素，最近有报道称，脂肪组织会产生 细胞外小泡(EVS)，可进入循环，影响全身葡萄糖代谢和 有助于胰岛素抵抗的发展。疾病进展过程中释放的脂肪组织EVS 与传统的可溶性脂肪因子相比，代表了预测T2D风险的优秀生物标记物， 由于脂肪组织EV含有生物活性物质，作用于靶器官以促进胰岛素抵抗和 最终实现T2D。脂肪组织EVS在血浆中也可能比传统的可溶性蛋白更稳定 可能更容易被血清蛋白酶活性降解的生物标记物。一种可以特异地 因此，捕捉和量化这些与T2D相关的脂肪组织EV可能有助于早期识别肥胖 在出现任何临床症状之前，T2D的高危人群。这项研究的总体目标是 发现和验证基于EVS的新生物标记物，用于识别肥胖高危人群 前T2D和T2D的发展。在目标1中，我们建议使用一种定量蛋白质组标记方法来 从肥胖患者分离的EV特异性或主要存在于脂肪组织中的EV蛋白 使用T2D、前T2D或不使用T2D。这些EV标志物可以作为探针的目标，专门检测 T2D或Pre-T2D相关EV水平。为了促进电动汽车在临床上的应用，我们将利用目标2 采用我们创新的纳米等离子增强散射分析来量化特定脂肪组织EVS 使用EV标志物探针与T2D风险增加相关，并建立检测T2D的最佳条件 或Pre-T2D相关脂肪组织EVS，标准化检测程序，并评估分析 检测的性能。为了更有效地将脂肪组织EV标志物转化为临床应用，我们 将验证用于检测血浆中脂肪组织EV的纳米等离子体法，以便及早检测 增加T2D风险以指导旨在先于组织延缓疾病进展的治疗干预 损坏。为此，目标3建议使用优化的纳米浆检测来评估脂肪组织 来自Bogalusa心脏研究长期队列的EVS对肥胖人群T2D的预测，并建立 机器学习模型，用于评估EV标记，包括单独评估和组合评估 T2D预测的已知因素。我有信心圆满完成提出的目标，并发展成为 在科布雷计划的支持下和导师团队的指导下的一名独立调查员。