Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases

Overview

Journal Metabolites

Publisher MDPI

Date 2019 Feb 17

PMID 30769897

Citations 44

Authors

Hayley Abbiss

Garth L Maker

Robert D Trengove

Affiliations

Soon will be listed here.

Abstract

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

Citing Articles

Metabolite pathway alterations identified by magnetic resonance metabolomics in a proximal tubular epithelial cell line treated with TGF-β1.

Humphries T, Lee S, Urquhart A, Vesey D, Micallef A, Winterford C Physiol Rep. 2025; 13(4):e70249.

PMID: 39957082 PMC: 11830627. DOI: 10.14814/phy2.70249.

Identification of urinary metabolites correlated with tacrolimus levels through high-precision liquid chromatography-mass spectrometry and machine learning algorithms in kidney transplant patients.

Burghelea D, Moisoiu T, Ivan C, Elec A, Munteanu A, Tabrea R Med Pharm Rep. 2025; 98(1):125-134.

PMID: 39949902 PMC: 11817595. DOI: 10.15386/mpr-2805.

Plasma proteome fingerprint in kidney diseases.

Nikolsky K, Kopylov A, Nakhod V, Potoldykova N, Enikeev D, Butkova T Front Mol Biosci. 2025; 11:1494779.

PMID: 39896931 PMC: 11782039. DOI: 10.3389/fmolb.2024.1494779.

Whole Blood Metabolomic Profiling of Mice with Tacrolimus-Induced Chronic Nephrotoxicity: NAD Depletion with Salvage Pathway Impairment.

Nishida S, Ishima T, Iwami D, Nagai R, Aizawa K Antioxidants (Basel). 2025; 14(1).

PMID: 39857396 PMC: 11760425. DOI: 10.3390/antiox14010062.

Volatile Organic Metabolites as Potential Biomarkers for Genitourinary Cancers: Review of the Applications and Detection Methods.

Holbrook K, Lee W Metabolites. 2025; 15(1).

PMID: 39852380 PMC: 11767221. DOI: 10.3390/metabo15010037.

References

Caspi R, Billington R, Ferrer L, Foerster H, Fulcher C, Keseler I . The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2015; 44(D1):D471-80. PMC: 4702838. DOI: 10.1093/nar/gkv1164. View

Xia J, Psychogios N, Young N, Wishart D . MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009; 37(Web Server issue):W652-60. PMC: 2703878. DOI: 10.1093/nar/gkp356. View

Broadhurst D, Goodacre R, Reinke S, Kuligowski J, Wilson I, Lewis M . Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics. 2018; 14(6):72. PMC: 5960010. DOI: 10.1007/s11306-018-1367-3. View

Qi S, Ouyang X, Wang L, Peng W, Wen J, Dai Y . A pilot metabolic profiling study in serum of patients with chronic kidney disease based on (1) H-NMR-spectroscopy. Clin Transl Sci. 2012; 5(5):379-85. PMC: 5439795. DOI: 10.1111/j.1752-8062.2012.00437.x. View

Kalim S, Rhee E . An overview of renal metabolomics. Kidney Int. 2016; 91(1):61-69. PMC: 5380230. DOI: 10.1016/j.kint.2016.08.021. View

Zhao Y, Cheng X, Wei F, Bai X, Tan X, Lin R . Intrarenal metabolomic investigation of chronic kidney disease and its TGF-β1 mechanism in induced-adenine rats using UPLC Q-TOF/HSMS/MS(E). J Proteome Res. 2012; 12(2):692-703. DOI: 10.1021/pr3007792. View

Yin P, Lehmann R, Xu G . Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem. 2015; 407(17):4879-92. PMC: 4471316. DOI: 10.1007/s00216-015-8565-x. View

Lee P, Terasaki P, Takemoto S, Lee P, Hung C, Chen Y . All chronic rejection failures of kidney transplants were preceded by the development of HLA antibodies. Transplantation. 2002; 74(8):1192-4. DOI: 10.1097/00007890-200210270-00025. View

Nkuipou-Kenfack E, Duranton F, Gayrard N, Argiles A, Lundin U, Weinberger K . Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease. PLoS One. 2014; 9(5):e96955. PMC: 4016198. DOI: 10.1371/journal.pone.0096955. View

10.

Yanes O, Tautenhahn R, Patti G, Siuzdak G . Expanding coverage of the metabolome for global metabolite profiling. Anal Chem. 2011; 83(6):2152-61. PMC: 3285547. DOI: 10.1021/ac102981k. View

11.

Dunn W, Bailey N, Johnson H . Measuring the metabolome: current analytical technologies. Analyst. 2005; 130(5):606-25. DOI: 10.1039/b418288j. View

12.

Kamijo A, Sugaya T, Hikawa A, Yamanouchi M, Hirata Y, Ishimitsu T . Urinary liver-type fatty acid binding protein as a useful biomarker in chronic kidney disease. Mol Cell Biochem. 2006; 284(1-2):175-82. DOI: 10.1007/s11010-005-9047-9. View

13.

Gigarel N, Frydman N, Burlet P, Kerbrat V, Tachdjian G, Fanchin R . Preimplantation genetic diagnosis for autosomal recessive polycystic kidney disease. Reprod Biomed Online. 2008; 16(1):152-8. DOI: 10.1016/s1472-6483(10)60569-x. View

14.

BLIGH E, Dyer W . A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959; 37(8):911-7. DOI: 10.1139/o59-099. View

15.

Schena F, Gesualdo L . Pathogenetic mechanisms of diabetic nephropathy. J Am Soc Nephrol. 2005; 16 Suppl 1:S30-3. DOI: 10.1681/asn.2004110970. View

16.

Hariharan S, McBride M, Cherikh W, Tolleris C, Bresnahan B, Johnson C . Post-transplant renal function in the first year predicts long-term kidney transplant survival. Kidney Int. 2002; 62(1):311-8. DOI: 10.1046/j.1523-1755.2002.00424.x. View

17.

Hocher B, Adamski J . Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol. 2017; 13(5):269-284. DOI: 10.1038/nrneph.2017.30. View

18.

Ferenbach D, Bonventre J . Acute kidney injury and chronic kidney disease: From the laboratory to the clinic. Nephrol Ther. 2016; 12 Suppl 1:S41-8. PMC: 5475438. DOI: 10.1016/j.nephro.2016.02.005. View

19.

Nicholson J, Lindon J . Systems biology: Metabonomics. Nature. 2008; 455(7216):1054-6. DOI: 10.1038/4551054a. View

20.

Sumner L, Amberg A, Barrett D, Beale M, Beger R, Daykin C . Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2013; 3(3):211-221. PMC: 3772505. DOI: 10.1007/s11306-007-0082-2. View