应用代谢组学和基于 Aptamer 的蛋白质组学确定因急性肾损伤住院的失代偿期肝硬化患者的病理生理差异

Giuseppe Cullaro, Andrew S. Allegretti, Kavish R. Patidar, Elizabeth C. Verna, Jennifer C. Lai
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摘要

摘要 方法 对在我院住院的 97 名患者进行病例对照研究。我们对入院 72 小时内获得的血清生物样本进行了基于适配体的蛋白质组学和代谢组学研究。我们使用方差分析比较了AKI表型(即HRS-AKI、ATN)和AKI恢复(sCr比基线下降0.3 mg/dL以内)的蛋白质组和代谢组,并对人口统计学和临床特征进行了调整。我们完成了随机森林(RF)分析,以确定与 AKI 表型和恢复相关的代谢物和蛋白质。我们还开发了拉索回归模型,以突出可提高诊断准确性的代谢物和蛋白质。结果方差分析显示,AKI 表型与代谢组学或蛋白质组学无差异,而 AKI 恢复状态则存在差异。我们的 RF 和 Lasso 分析表明,代谢组学能提高 AKI 诊断和恢复的诊断准确性,而基于适配体的蛋白质组学能提高 AKI 恢复的诊断准确性。讨论:我们的分析提供了对病理生理途径的新见解,突出了肝硬化伴 HRS-AKI 和 ATN 患者之间代谢组学和蛋白质组学的相似性,同时也发现了 AKI 恢复和未恢复患者之间的差异。
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Applying Metabolomics and Aptamer-based Proteomics to Determine Pathophysiologic Differences in Decompensated Cirrhosis Patients Hospitalized with Acute Kidney Injury
Abstract Methods A case-control study of 97 patients hospitalized at our institution. We performed aptamer-based proteomics and metabolomics on serum biospecimens obtained within 72 hours of admission. We compared the proteome and metabolome by the AKI phenotype (i.e., HRS-AKI, ATN) and by AKI recovery (decrease in sCr within 0.3 mg/dL of baseline) using ANCOVA analyses adjusting for demographics and clinical characteristics. We completed Random Forest (RF) analyses to identify metabolites and proteins associated with AKI phenotype and recovery. Lasso regression models were developed to highlight metabolites and proteins could improve diagnostic accuracy. Results: ANCOVA analyses showed no metabolomic or proteomic differences by AKI phenotype while identifying differences by AKI recovery status. Our RF and Lasso analyses showed that metabolomics can improve the diagnostic accuracy of both AKI diagnosis and recovery, and aptamer-based proteomics can enhance the diagnostic accuracy of AKI recovery. Discussion: Our analyses provide novel insight into pathophysiologic pathways, highlighting the metabolomic and proteomic similarities between patients with cirrhosis with HRS-AKI and ATN while also identifying differences between those with and without AKI recovery.
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