Metabolomic Biomarkers of CKD

描述(由申请人提供)：这项拟议研究的长期目标是确定慢性肾脏疾病(CKD)的血浆和/或尿液生物标记物，这些生物标记物比目前的肾功能指标提供更好的诊断、机制和预后信息。在分析化学最新进展的催化下，加上计算能力，这一建议将代谢物分析与访问特征良好的临床队列相结合，以满足广泛公认的对CKD改进生物标记物的需求。在这种情况下，小分子代谢物是很有希望的生物标志物，原因有几个：1)CKD是一种由小分子代谢物的异常过滤、重吸收和排泄定义的状况；2)CKD处于各种代谢性疾病途径的交叉点，包括高血压、胰岛素抵抗、血脂异常和炎症：循环代谢物本身可能作为调节信号参与控制血压、能量平衡和白细胞募集；以及3)高通量代谢物图谱在当前技术下是实用和可行的。我们假设，与正常人相比，CKD患者具有不同的血浆和尿代谢物特征，与CKD相关的代谢物亚集将预测CKD的进展。我们的假设将通过以下特定目标进行验证：目的1.使用基于LC-MS/MS的靶向代谢组学平台，在CKD 1-5期糖尿病和非糖尿病肾病患者中发现新的CKD生物标志物。目的2.在CKD受试者的前瞻性队列中验证新的CKD血、尿生物标志物，验证它们与CKD在基线时的定量和定性关联(目标2A)，并证明它们预测CKD随时间进展的能力(目标2B)。与这些目标无关，我们相信，这些初步努力在CKD严重程度和病因的范围内描绘血浆和尿液，将对正在进行的注释人类代谢组的努力做出有价值的贡献，并将为未来人类疾病的代谢组学研究提供信息。目前的肾脏疾病标记物无法可靠地检测出早期肾脏疾病，也无法预测哪些人的肾功能会恶化。新技术现在可以对肾脏疾病的新标志物进行快速和广泛的筛查。更好的肾脏疾病标志物允许更早的诊断，将使更早的治疗和规划成为可能，并将有助于努力发现肾脏疾病的新疗法。

DESCRIPTION (provided by applicant): The long-term goal of this proposed research is to identify plasma and/or urine biomarkers of chronic kidney disease (CKD) that provide better diagnostic, mechanistic, and prognostic information than current indices of kidney function. Catalyzed by recent advances in analytical chemistry, coupled with computational power, this proposal integrates metabolite profiling with access to well-characterized clinical cohorts to address a widely recognized need for improved biomarkers of CKD. Small molecule metabolites are promising biomarkers in this context for several reasons: 1) CKD is a condition defined by aberrant filtration, re-absorption, and excretion of small molecule metabolites; 2) CKD is at the intersection of various metabolic disease pathways, including hypertension, insulin resistance, dyslipidemia, and inflammation: circulating metabolites may themselves participate as regulatory signals, as in the control of blood pressure, energy homeostasis, and leukocyte recruitment; and 3) High throughput metabolite profiling is practical and feasible with current technology. We hypothesize that individuals with CKD have distinct plasma and urine metabolites profile as compared to normals and that a subset of metabolites associated with CKD will predict CKD progression. Our hypothesis will be tested by the following specific aims: Aim 1. To use a targeted, LC-MS/MS-based metabolomics platform to discover novel plasma and urine biomarkers of CKD in subjects with CKD stages 1-5 with both diabetic and non-diabetic nephropathy. Aim 2. To validate novel plasma and urine biomarkers of CKD in a prospective cohort of CKD subjects by verifying their quantitative and qualitative association with CKD at baseline (Aim 2A), and demonstrating their ability to predict CKD progression over time (Aim 2B). Independent of these aims, we believe these initial efforts to profile plasma and urine across a spectrum of CKD severity and etiology will be a valuable contribution to ongoing efforts to annotate the human metabolome, and will inform future metabolomics studies of human disease. Current markers of kidney disease fail to reliably detect early kidney disease and are unable to predict which individuals will have worsening kidney function. New technologies now allow for rapid and broad screening for new markers of kidney disease. Better markers of kidney disease that permit earlier diagnosis would allow earlier treatment and planning, and would help efforts to discover new treatments for kidney disease.