Predictive Modeling of Metabolomics data for the Identification of Biomarkers in Chronic Kidney Disease

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Published Jun 26, 2023
DOI: https://doi.org/10.18103/mra.v11i6.3894

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Main Article Content

Ambulge Sheetal
Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Panjab University, Sector 14, Chandigarh - 160014, India.
Ambati Sekhar Reddy
Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Panjab University, Sector 14, Chandigarh - 160014, India.
Pooja Arora
Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Panjab University, Sector 14, Chandigarh - 160014, India.
Veena Puri
Center for Systems Biology and Bioinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Panjab University, Sector 14, Chandigarh - 160014, India.
Prasad V. Bharatam
Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER), Panjab University, Sector 14, Chandigarh - 160014, India.

Abstract

Chronic kidney disease  is a specific type of Kidney Disease in which, a gradual loss of Kidney Function over a period of 3-30 months is noticeable. Early detection is imperative to prevent this catastrophic event and initiate treatment that may mitigate renal injury. Metabolomics data in Chronic Kidney Disease carries a lot of information about biomarkers. However, it is not clear which of these biomarkers are significant, biostatistical analysis of metabolomics data might provide the clues. In this work, an attempt has been made to find novel biomarkers that may be responsible for causing Chronic Kidney Disease by employing bioinformatics and advanced computational tools. The Chronic Kidney Disease data of the patients (in stages 3 and 4) was selected and data was segregated based on renal and cardiovascular parameters. The study consisted 441 patients and 293 metabolites. Subsequently the identification of top metabolites (as biomarkers) was carried out using statistical methods like t-test, Principal component analysis and partial least square analysis. Nine biomarkers were identified from these statistical analyses i.e Galacturonic acid, p-cresol, L-serine, L-glutamine, Lactose, 2-O-Glycerol-.alpha.-d-galactopyranoside, hexa-TMS, Butanoic acid, 2,4-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester, Pseudo uridine penta-tms and Myo-inositol. The Reactivity of identified metabolites was confirmed by using quantum chemistry calculations in Gaussian software. Heat Map was constructed to find out the variations in concentrations of biomarkers in healthy and CKD patients and the showed the higher concentrations of L-serine, Galacturonic acid , L-glutamine and lower concentrations of Pseudo uridine penta-tms, Butanoic acid, 2,4-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester , 2-O-Glycerol-.alpha.-d-galactopyranoside, hexa-TMS , Myo-inositol , p-cresol , Lactose in Death Patients. The biological significance of identified top metabolites has been evaluated by identifying the metabolic pathways in which the metabolites are involved. The metabolites which were found to be toxic are pseudouridine, L-glutamine, and galactouronic acid as per the previous reported literature. The Variations in concentration of these metabolites are responsible for the Death of patient with Chronic Kidney Disease.

Keywords: Kidney Disease, Chronic Kidney Disease, Predictive Modeling

Article Details

How to Cite
SHEETAL, Ambulge et al. Predictive Modeling of Metabolomics data for the Identification of Biomarkers in Chronic Kidney Disease. Medical Research Archives, [S.l.], v. 11, n. 6, june 2023. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/3894>. Date accessed: 21 nov. 2024. doi: https://doi.org/10.18103/mra.v11i6.3894.
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Issue
Vol 11 No 6 (2023): June Issue, Vol.11, Issue 6
Section
Research Articles

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