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

Article Sidebar

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

Downloads

Download data is not yet available.

Submit your own article

Register as an author to reserve your spot in the next issue of the Medical Research Archives.

Author Registration

Join the Society

The European Society of Medicine is more than a professional association. We are a community. Our members work in countries across the globe, yet are united by a common goal: to promote health and health equity, around the world.

Join Europe’s leading medical society and discover the many advantages of membership, including free article publication.

Membership

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: 16 may 2024. doi: https://doi.org/10.18103/mra.v11i6.3894.
ABNT APA BibTeX CBE EndNote - EndNote format (Macintosh & Windows) MLA ProCite - RIS format (Macintosh & Windows) RefWorks Reference Manager - RIS format (Windows only) Turabian
Issue
Vol 11 No 6 (2023): June Issue, Vol.11, Issue 6
Section
Research Articles

The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.

 

References

1. Levey AS, Eckardt KU, Tsukamoto Y, et al. Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int. 2005;67(6):2089-2100.

2. Rettig RA, Norris K, Nissenson AR. Chronic kidney disease in the United States: a public policy imperative. Clin J Am Soc Nephrol. 2008;3(6):1902-1910.

3. Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17(7):451-459.

4. Mayeux R. Biomarkers: potential uses and limitations. NeuroRx. 2004;1(2):182-188.

5. Titan SM, Venturini G, Padilha K, et al. Metabolites related to eGFR: Evaluation of candidate molecules for GFR estimation using untargeted metabolomics. Clin Chim Acta. 2019;489:242-248.

6. Ma J, Karnovsky A, Afshinnia F, et al. Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease. Bioinformatics. 2019;35(18):3441-3452.

7. Afshinnia F, Rajendiran TM, Soni T, et al. Impaired β-Oxidation and Altered Complex Lipid Fatty Acid Partitioning with Advancing CKD. J Am Soc Nephrol. 2018;29(1):295-306.

8. Afshinnia F, Rajendiran TM, Karnovsky A, et al. Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort [published correction appears in Kidney Int Rep. 2017 Sep 18; 2(6):1265]. Kidney Int Rep. 2016;1(4):256-268.

9. Tsuruya K, Yoshida H, Nagata M, et al. Association of the triglycerides to high-density lipoprotein cholesterol ratio with the risk of chronic kidney disease: analysis in a large Japanese population. Atherosclerosis. 2014;233(1):260-267.

10. Xi B, Gu H, Baniasadi H, Raftery D. Statistical analysis and modeling of mass spectrometry-based metabolomics data. Methods Mol Biol. 2014;1198:333-353.

11. Szymańska E, Saccenti E, Smilde AK, Westerhuis JA. Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics. 2012; 8(Suppl 1):3-16.

12. Almasoud, M, Ward, T.E. Detection of Chronic Kidney Disease using Machine Learning Algorithms with Least Number of Predictors. International Journal of Advanced Computer Science and Applications. 2019;10(8)

13. Cheadle C, Vawter MP, Freed WJ, Becker KG. Analysis of microarray data using Z score transformation. J Mol Diagn. 2003;5(2):73-81.

14. Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics. 2001;17(6):509-519.

15. Bartel J, Krumsiek J, Theis FJ. Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J. 2013;4:e201301009. Published 2013 Mar 22.

16. Szymańska E, Saccenti E, Smilde AK, Westerhuis JA. Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics. 2012; 8(Suppl 1):3-16.

17. Padmanabhan J, Parthasarathi R, Subramanian V, Chattaraj PK. Electrophilicity-based charge transfer descriptor. J Phys Chem A. 2007;111(7):1358-1361.

18. Patlewicz G, Jeliazkova N, Safford RJ, Worth AP, Aleksiev B. An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res. 2008;19(5-6):495-524.

19. Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37(Web Server issue):W652-W660.


20. Dean ED, Li M, Prasad N, et al. Interrupted Glucagon Signaling Reveals Hepatic α Cell Axis and Role for L-Glutamine in α Cell Proliferation. Cell Metab. 2017;25(6):1362-1373.e5.

21. Daniel E, Anitha, J. Gnanaraj J. Optimum laplacian wavelet mask based medical image using hybrid cuckoo search – grey wolf optimization algorithm. Knowledge-Based Systems. 2017; 131:58-69.

22. Ma J, Liang P, Yu W, Chen C, Guo X, Wu J, Jiang J. Infrared and visible image fusion via detail preserving adversarial learning. Inf. Fusion. 2020; 54: 85-98.

23. Zhang G, Darshi M, Sharma K. The Warburg Effect in Diabetic Kidney Disease. Semin Nephrol. 2018;38(2):111-120.