Nuclear Magnetic Resonance-Based Metabolomics Approach Revealed the Intervention Effect of Using Complementary and Alternative Medicine (CAM) by CKD Patients

Nikhil Gupta; Deependra Kumar Yadav; Sonam Gautam; Ashish Kumar; Dinesh Kumar; Narayan Prasad

doi:10.1021/acsomega.2c06469

Nuclear Magnetic Resonance-Based Metabolomics Approach Revealed the Intervention Effect of Using Complementary and Alternative Medicine (CAM) by CKD Patients

ACS Omega. 2023 Feb 16;8(8):7722-7737. doi: 10.1021/acsomega.2c06469. eCollection 2023 Feb 28.

Authors

Nikhil Gupta^{1

2}, Deependra Kumar Yadav³, Sonam Gautam³, Ashish Kumar², Dinesh Kumar¹, Narayan Prasad³

Affiliations

¹ Centre of Biomedical Research (CBMR), Lucknow, Uttar Pradesh 226014, India.
² Department of Chemistry, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India.
³ Department of Nephrology, SGPGIMS, Lucknow, Uttar Pradesh 226014, India.

Abstract

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 ¹H 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., p < 0.05) using either Student t-test or ANOVA statistics. PLS-DA models were capable of clustering CKD and CAM-CKD with considerably high values of Q ² and R ². 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.