Prognostic Biomarkers in Chronic Kidney Disease

慢性肾脏病的预后生物标志物

• 批准号: 9749995
• 负责人: Farsad Afshinnia
• 金额: $ 16.63万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-17 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9749995
• 关键词: 21 year old; Address; Age; Amino Acids; Area; Biological Markers; Cardiovascular Diseases; Cardiovascular system; Chronic Kidney Failure; Chronic Kidney Insufficiency; Citric Acid; Citric Acid Cycle; Classification; Clinical; Cohort Studies; Comorbidity; Control Groups; Creatinine; Data; Data Collection; Detection; Diabetes Mellitus; Diagnostic; Discrimination; Disease; Disease Progression; End stage renal failure; Enzymes; European; Exclusion Criteria; Excretory function; Fasting; Framingham Heart Study; Gene Expression; Genes; Glomerular Filtration Rate; Goals; Injury; Kidney; Laboratories; Link; Lipids; Logistic Regressions; Mass Spectrum Analysis; Measurement; Measures; Metabolic; Metabolic Diseases; Metabolic Marker; Metabolic Pathway; Methods; Nucleotides; Outcome; Participant; Pathway interactions; Patient Care; Patient-Focused Outcomes; Patients; Performance; Phase; Physical Examination; Preventive Intervention; Process; Prognostic Marker; Proteins; Public Health; Race; Receiver Operating Characteristics; Regression Analysis; Regulation; Renal Tissue; Renal function; Reporting; Resources; Risk; Sample Size; Sampling; Serum; Specificity; Testing; Therapeutic Intervention; Time; Tissues; United States; Validation; acylcarnitine; base; biobank; bioinformatics tool; biomarker identification; biomarker panel; candidate marker; cardiovascular risk factor; care outcomes; clinical care; clinical phenotype; cohort; demographics; design; diabetic patient; differential expression; high risk; high risk population; improved; inclusion criteria; indexing; kidney biopsy; metabolomics; mortality; novel; novel marker; outcome prediction; predictive modeling; prognostic; prognostic value; public health relevance; sample collection; sex; solute; statistics; study population; transcriptomics; urea cycle; urinary

DESCRIPTION (provided by applicant): Identification of novel mechanistic markers with prognostic value in clinical course of chronic kidney disease (CKD) will provide a better understanding of disease process and improve care of the patients by identification of high risk groups at earlier stages. Importantly, identification of mechanistic pathways will be amenable to novel preventive or therapeutic interventions. This study is aimed at comparing the predictive power of ANCUAL (Amino acids, Nucleotides, intermediates of Citric acid and Urea cycles, Acylcarnitines, and Lipids) metabolic panel to predict progression of CKD by comparing their area under Receiver Operating Characteristics (ROC) curves with those of the traditional markers (serum creatinine, urinary protein, eGFR) at early stage. We also aim to explore the link between the proposed metabolic markers with the renal tissue expression level of genes involved in regulation of these markers. Methods: This will be a nested case-cohort study. Study populations consist of participants of Chronic Renal Insufficiency Cohort (CRIC) for measurement of metabolites, and Clinical Phenotyping Resource and Biobank Core (CPROBE) for transcriptomics- metabolomics integrative analysis. Inclusion criteria are entry age of 21 to 74 years with eGFR of 30-70 for CRIC participants, and entry eGFR of 30 mL/min or higher for CPROBE participants. Exclusion criteria are the same as CRIC and CPROBE core studies. Sampling: From CRIC participants; case group is defined as 100 patients who have progressed to ESRD including 50 patients with diabetes and 50 patients without diabetes. Control group will be 100 patients matched by age, sex, race, diabetes, and baseline eGFR (N=200). This sampling will be replicated to get another 200 patients from CRIC in the validation phase (total=400). From CPROBE participants; 42 participants with available gene expression data obtained from kidney biopsy will be selected. Data collection: Starting with CRIC, fasting serum samples from baseline will be collected. First, samples will be subjected to targeted metabolomics analysis by mass spectrometry (MS) for quantification of ANCUAL panel, in the first 200 CRIC participants. This will be followed by an untargeted metabolomic analysis for expansion of the initial panel, followed by an orthogonal quantification of the newly identified prognostic metabolites to validate the discovery of the new compounds from untargeted platform. The expanded ANCUAL panel will then undergo replication in a targeted platform in another 200 independent samples from CIRC to validate the final panel. At last step the final panel will be quantified in the baseline serum samples of CPROBE participants in an attempt to integrate with their transcriptomics data. Predictors are the expanded ANCUAL panel derived from MS. Outcome is incident ESRD. Covariates include demographics, laboratory values, comorbidities, physical examination findings and anthropometric measures from the time of sample collection. Analysis: Multiple logistic regression analysis will be applied to identify the independent components of a multi panel marker among the differentially expressed baseline panel. C-statistics will be applied to compare the area under Receiver Operating Characteristics (ROC) curve of the multi-panel marker with that if the traditional biomarkers for discrimination of progressors from non-progressors. Integrated discrimination improvement, net reclassification index, and C-statistics will be applied to assess the classification improvement of the expanded panel compared to baseline panel. Risk of ESRD by quartiles of the proposed panel will be estimated using logistic regression adjusting for relevant prognostic covariates. Gene and metabolic set enrichment analysis will be applied to test enrichment of differentially regulated metabolic pathways. Bioinformatics tools will be used for pathway recognition.

描述（由申请方提供）：在慢性肾脏病（CKD）临床病程中鉴定具有预后价值的新型机制标志物将更好地了解疾病过程，并通过在早期阶段鉴定高风险组来改善患者护理。重要的是，机制途径的鉴定将适用于新的预防或治疗干预措施。本研究旨在通过比较ANCUAL（氨基酸、核苷酸、柠檬酸和尿素循环的中间产物、酰基肉毒碱和脂质）代谢组与传统标志物（血清肌酐、尿蛋白、eGFR）在早期阶段的受试者工作特征（ROC）曲线下面积，比较其预测CKD进展的预测能力。我们还旨在探索拟议的代谢标志物与参与这些标志物调控的基因的肾组织表达水平之间的联系。方法：这将是一项巢式病例队列研究。研究人群包括慢性肾功能不全队列（CRIC）（用于测量代谢物）和临床表型资源和生物库核心（CPROBE）（用于转录组学-代谢组学综合分析）的受试者。入选标准为CRIC参与者的入组年龄为21至74岁，eGFR为30-70，CPROBE参与者的入组eGFR为30 mL/min或更高。排除标准与CRIC和CPROBE核心研究相同。取样：审评委与会者;病例组为100例进展为ESRD的患者，其中糖尿病患者50例，非糖尿病患者50例。对照组为100例按年龄、性别、人种、糖尿病和基线eGFR匹配的患者（N=200）。将重复这一抽样，以便在验证阶段从审评委中再获得200名患者（总计=400名）。从CPROBE受试者中;将选择42名具有从肾活检获得的可用基因表达数据的受试者。数据收集：从CRIC开始，将收集基线空腹血清样本。首先，将对前200名CRIC参与者的样本进行质谱法（MS）靶向代谢组学分析，以量化ANCUAL组。随后将进行非靶向代谢组学分析，以扩展初始组，然后对新鉴定的预后代谢物进行正交定量，以验证从非靶向平台发现的新化合物。然后，扩展的ANCUAL面板将在来自CIRC的另外200个独立样本中在目标平台上进行复制，以验证最终面板。在最后一步，将在CPROBE参与者的基线血清样本中对最终组进行定量，以尝试与其转录组学数据整合。预测因子为MS衍生的扩展ANCUAL面板。结局为ESRD事件。协变量包括人口统计学、实验室检查值、合并症、体格检查结果和样本采集时的人体测量指标。分析结果：将应用多元逻辑回归分析来鉴定差异表达基线组中多组标志物的独立组分。将应用C-统计量比较多组标志物的受试者工作特征（ROC）曲线下面积与传统生物标志物的受试者工作特征曲线下面积， 进步者与非进步者将应用综合区分改善、净重新分类指数和C统计量来评估扩展组与基线组相比的分类改善。将使用logistic回归调整相关预后协变量，按拟定组的四分位数估计ESRD风险。基因和代谢集富集分析将用于测试差异调节的代谢途径的富集。生物信息学工具将用于途径识别。

项目成果

Biliary reconstruction in liver transplant patients with primary sclerosing cholangitis, duct-to-duct or Roux-en-Y?

• DOI: 10.1111/ctr.12964
• 发表时间: 2017-06-01
• 期刊: CLINICAL TRANSPLANTATION
• 影响因子: 2.1
• 作者: Shamsaeefar, Alireza;Shafiee, Mohammad;Malekhosseini, Seyed Ali
• 通讯作者: Malekhosseini, Seyed Ali

Tryptophan levels associate with incident cardiovascular disease in chronic kidney disease.

• DOI: 10.1093/ckj/sfaa031
• 发表时间: 2021-04
• 期刊: Clinical kidney journal
• 影响因子: 4.6
• 作者: Konje VC;Rajendiran TM;Bellovich K;Gadegbeku CA;Gipson DS;Afshinnia F;Mathew AV;Michigan Kidney Translational Core CPROBE Investigator Group
• 通讯作者: Michigan Kidney Translational Core CPROBE Investigator Group