Gupta Nikhil, Yadav Deependra Kumar, Gautam Sonam, Kumar Ashish, Kumar Dinesh, Prasad Narayan
Centre of Biomedical Research (CBMR), Lucknow, Uttar Pradesh 226014, India.
Department of Chemistry, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India.
ACS Omega. 2023 Feb 16;8(8):7722-7737. doi: 10.1021/acsomega.2c06469. eCollection 2023 Feb 28.
Chronic kidney disease (CKD) is the end point of a number of systemic chronic diseases. The prevalence of CKD is increasing worldwide and recent epidemiological studies are showing the high prevalence of renal failure in CKD patients using complementary and alternative medicines (CAMs). Clinicians believe that biochemical profiles of CKD patients using CAM (referred here to as CAM-CKD) may be different compared to those on standard clinical treatment and should be managed differently. The present study aims to explore the potential of the NMR-based metabolomics approach to reveal the serum metabolic disparity between CKD and CAM-CKD patients with respect to normal control (NC) subjects and if the differential metabolic patterns can provide rationale for the efficacy and safety of standard and/or alternative therapies. Serum samples were obtained from 30 CKD patients, 43 CAM-CKD patients, and 47 NC subjects. The quantitative serum metabolic profiles were measured using 1D H CPMG NMR experiments performed at 800 MHz NMR spectrometer. The serum metabolic profiles were compared using various multivariate statistical analysis tools available on MetaboAnalyst (freely available web-based software) such as partial least-squares discriminant analysis (PLS-DA) and random forest (a machine learning) classification method. The discriminatory metabolites were identified based on variable importance in projection (VIP) statistics and further evaluated for statistical significance (i.e., < 0.05) using either Student -test or ANOVA statistics. PLS-DA models were capable of clustering CKD and CAM-CKD with considerably high values of and . Compared to CAM-CKD patients, the sera of CKD patients were characterized by (a) elevated levels of urea, creatinine, citrate, glucose, glycerol, and phenylalanine and phenylalanine-to-tyrosine ratio (PTR) and (b) decreased levels of various amino acids (such leucine, isoleucine, valine, and alanine), high-density lipoproteins, lactate, and acetate. These changes suggested that CKD patients manifest severe oxidative stress, hyperglycemia (with dampened glycolysis), increased protein energy wasting, and reduced lipid/membrane metabolism. Statistically significant and strong positive correlation of PTR with serum creatinine levels suggested the role of oxidative stress in the progression of kidney disease. Significant differences in metabolic patterns between CKD and CAM-CKD patients were observed. With respect to NC subjects, the serum metabolic changes were more aberrant in CKD patients compared to CAM-CKD patients. The aberrant metabolic changes in CKD patients with manifestations of higher oxidative stress compared to CAM-CKD patients could explain clinical discrepancies between CKD and CAM-CKD patients and further advocate the use of different treatment strategies for CKD and CAM-CKD patients.
慢性肾脏病(CKD)是多种全身性慢性疾病的终点。CKD在全球的患病率正在上升,最近的流行病学研究表明,使用补充和替代医学(CAMs)的CKD患者中肾衰竭的患病率很高。临床医生认为,使用CAM的CKD患者(以下简称CAM-CKD)的生化特征可能与接受标准临床治疗的患者不同,应该采用不同的管理方式。本研究旨在探讨基于核磁共振的代谢组学方法揭示CKD患者与CAM-CKD患者相对于正常对照(NC)受试者血清代谢差异的潜力,以及差异代谢模式是否能为标准和/或替代疗法的疗效和安全性提供依据。从30例CKD患者、43例CAM-CKD患者和47例NC受试者中采集血清样本。使用在800 MHz核磁共振波谱仪上进行的一维氢碳化学位移成像核磁共振实验测量血清定量代谢谱。使用MetaboAnalyst(免费的基于网络的软件)上可用的各种多元统计分析工具,如偏最小二乘判别分析(PLS-DA)和随机森林(一种机器学习)分类方法,比较血清代谢谱。基于投影变量重要性(VIP)统计确定具有鉴别作用的代谢物,并使用学生检验或方差分析统计进一步评估其统计学意义(即P<0.05)。PLS-DA模型能够以相当高的R2和Q2值对CKD和CAM-CKD进行聚类。与CAM-CKD患者相比,CKD患者血清的特征是:(a)尿素、肌酐、柠檬酸盐、葡萄糖、甘油、苯丙氨酸和苯丙氨酸与酪氨酸比值(PTR)升高;(b)各种氨基酸(如亮氨酸、异亮氨酸、缬氨酸和丙氨酸)、高密度脂蛋白、乳酸和乙酸水平降低。这些变化表明,CKD患者表现出严重的氧化应激、高血糖(糖酵解受抑制)、蛋白质能量消耗增加以及脂质/膜代谢减少。PTR与血清肌酐水平在统计学上具有显著且强烈的正相关,表明氧化应激在肾脏疾病进展中的作用。观察到CKD和CAM-CKD患者之间代谢模式存在显著差异。相对于NC受试者,CKD患者的血清代谢变化比CAM-CKD患者更异常。与CAM-CKD患者相比,表现出更高氧化应激的CKD患者的异常代谢变化可以解释CKD和CAM-CKD患者之间的临床差异,并进一步主张对CKD和CAM-CKD患者采用不同的治疗策略。