• This record comes from PubMed

Mass Spectrometry-Based Lipidomics Reveals Differential Changes in the Accumulated Lipid Classes in Chronic Kidney Disease

. 2021 Apr 27 ; 11 (5) : . [epub] 20210427

Status PubMed-not-MEDLINE Language English Country Switzerland Media electronic

Document type Journal Article

Grant support
2015/19/B/ NZ2/02450 National Science Centre, Poland

Chronic kidney disease (CKD) is characterized by the progressive loss of functional nephrons. Although cardiovascular disease (CVD) complications and atherosclerosis are the leading causes of morbidity and mortality in CKD, the mechanism by which the progression of CVD accelerates remains unclear. To reveal the molecular mechanisms associated with atherosclerosis linked to CKD, we applied a shotgun lipidomics approach fortified with standard laboratory analytical methods and gas chromatography-mass spectrometry technique on selected lipid components and precursors to analyze the plasma lipidome in CKD and classical CVD patients. The MS-based lipidome profiling revealed the upregulation of triacylglycerols in CKD and downregulation of cholesterol/cholesteryl esters, sphingomyelins, phosphatidylcholines, phosphatidylethanolamines and ceramides as compared to CVD group and controls. We have further observed a decreased abundance of seven fatty acids in CKD with strong inter-correlation. In contrast, the level of glycerol was elevated in CKD in comparison to all analyzed groups. Our results revealed the putative existence of a functional causative link-the low cholesterol level correlated with lower estimated glomerular filtration rate and kidney dysfunction that supports the postulated "reverse epidemiology" theory and suggest that the lipidomic background of atherosclerosis-related to CKD is unique and might be associated with other cellular factors, i.e., inflammation.

See more in PubMed

Levin A., Stevens P., Bilous R. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. Suppl. 2013;3:1e150. PubMed

Yamamoto S., Kon V. Mechanisms for increased cardiovascular disease in chronic kidney dysfunction. Curr. Opin. Nephrol. Hypertens. 2009;18:181–188. doi: 10.1097/MNH.0b013e328327b360. PubMed DOI PMC

Ecder T. Early diagnosis saves lives: Focus on patients with chronic kidney disease. Kidney Int. Suppl. 2013;3:335–336. doi: 10.1038/kisup.2013.70. PubMed DOI PMC

Briasoulis A., Bakris G.L. Chronic Kidney Disease as a Coronary Artery Disease Risk Equivalent. Curr. Cardiol. Rep. 2013;15:340. doi: 10.1007/s11886-012-0340-4. PubMed DOI

De Santo N.G., Cirillo M., Perna A., De Santo L.S., Anastasio P., Pollastro M.R., De Santo R.M., Iorio L., Cotrufo M., Rossi F. The heart in uremia: Role of hypertension, hypotension, and sleep apnea. Am. J. Kidney Dis. 2001;38:S38–S46. doi: 10.1053/ajkd.2001.27395. PubMed DOI

Olechnowicz-Tietz S., Gluba A., Paradowska A., Banach M., Rysz J. The risk of atherosclerosis in patients with chronic kidney disease. Int. Urol. Nephrol. 2013;45:1605–1612. doi: 10.1007/s11255-013-0407-1. PubMed DOI PMC

Levey A.S., Beto J.A., Coronado B.E., Eknoyan G., Foley R.N., Kasiske B.L., Klag M.J., Mailloux L.U., Manske C.L., Meyer K.B., et al. Controlling the epidemic of cardiovascular disease in chronic renal disease: What do we know? What do we need to learn? Where do we go from here? National Kidney Foundation Task Force on Cardiovascular Disease. Am. J. Kidney Dis. 1998;32:853–906. doi: 10.1016/S0272-6386(98)70145-3. PubMed DOI

Schiffrin E.L., Lipman M.L., Mann J.F.E. Chronic kidney disease: Effects on the cardiovascular system. Circulation. 2007;116:85–97. doi: 10.1161/CIRCULATIONAHA.106.678342. PubMed DOI

De Jager D.J., Grootendorst D.C., Jager K.J., Van Dijk P.C., Tomas L.M.J., Ansell D., Collart F., Finne P., Heaf J.G., De Meester J., et al. Cardiovascular and noncardiovascular mortality among patients starting dialysis. JAMA J. Am. Med. Assoc. 2009;302:1782–1789. doi: 10.1001/jama.2009.1488. PubMed DOI

Manjunath C.N., Rawal J.R., Irani P.M., Madhu K. Atherogenic dyslipidemia. Indian J. Endocrinol. Metab. 2013;17:969–976. doi: 10.4103/2230-8210.122600. PubMed DOI PMC

Kalantar-Zadeh K., Block G., Humphreys M.H., Kopple J.D. Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int. 2003;63:793–808. doi: 10.1046/j.1523-1755.2003.00803.x. PubMed DOI

Liu Y., Coresh J., Eustace J.A., Longenecker J.C., Jaar B., Fink N.E., Tracy R.P., Powe N.R., Klag M.J. Association Between Cholesterol Level and Mortality in Dialysis Patients. JAMA. 2004;291:451. doi: 10.1001/jama.291.4.451. PubMed DOI

Baigent C., Landray M.J., Wheeler D.C. Misleading associations between cholesterol and vascular outcomes in dialysis patients: The need for randomized trials. Semin. Dial. 2007;20:498–503. doi: 10.1111/j.1525-139X.2007.00340.x. PubMed DOI

Nogueira J., Weir M. The unique character of cardiovascular disease in chronic kidney disease and its implications for treatment with lipid-lowering drugs. Clin. J. Am. Soc. Nephrol. 2007;2:766–785. doi: 10.2215/CJN.04131206. PubMed DOI

Valdivielso J.M., Rodríguez-Puyol D., Pascual J., Barrios C., Bermúdez-López M., Sánchez-Niño M.D., Pérez-Fernández M., Ortiz A. Atherosclerosis in Chronic Kidney Disease. Arterioscler. Thromb. Vasc. Biol. 2019;39:1938–1966. doi: 10.1161/ATVBAHA.119.312705. PubMed DOI

Cai Q., Mukku V.K., Ahmad M. Coronary artery disease in patients with chronic kidney disease: A clinical update. Curr. Cardiol. Rev. 2013;9:331–339. doi: 10.2174/1573403X10666140214122234. PubMed DOI PMC

Tannock L. Dyslipidemia in Chronic Kidney Disease. MDText.com, Inc.; Portland, OR, USA: 2000.

Obialo C.I., Ofili E.O., Norris K.C. Statins and Cardiovascular Disease Outcomes in Chronic Kidney Disease: Reaffirmation vs. Repudiation. Int. J. Environ. Res. Public Health. 2018;15:2733. doi: 10.3390/ijerph15122733. PubMed DOI PMC

Luczak M., Formanowicz D., Marczak Ł., Suszyńska-Zajczyk J., Pawliczak E., Wanic-Kossowska M., Stobiecki M. ITRAQ-based proteomic analysis of plasma reveals abnormalities in lipid metabolism proteins in chronic kidney disease-related atherosclerosis. Sci. Rep. 2016;6:32511. doi: 10.1038/srep32511. PubMed DOI PMC

Reis A., Rudnitskaya A., Chariyavilaskul P., Dhaun N., Melville V., Goddard J., Webb D.J., Pitt A.R., Spickett C.M. Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease. J. Lipid Res. 2015;56:413–422. doi: 10.1194/jlr.M055624. PubMed DOI PMC

Afshinnia F., Rajendiran T.M., Karnovsky A., Soni T., Wang X., Xie D., Yang W., Shafi T., Weir M.R., He J., et al. Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort. Kidney Int. Rep. 2016;1:256–268. doi: 10.1016/j.ekir.2016.08.007. PubMed DOI PMC

Ekroos K., Jänis M., Tarasov K., Hurme R., Laaksonen R. Lipidomics: A Tool for Studies of Atherosclerosis. Curr. Atheroscler. Rep. 2010;12:273–281. doi: 10.1007/s11883-010-0110-y. PubMed DOI PMC

Zhao Y.-Y., Cheng X., Lin R.-C. Lipidomics Applications for Discovering Biomarkers of Diseases in Clinical Chemistry. Int. Rev. Cell Mol. Biol. 2014;313:1–26. doi: 10.1016/B978-0-12-800177-6.00001-3. PubMed DOI

Holčapek M., Červená B., Cífková E., Lísa M., Chagovets V., Vostálová J., Bancířová M., Galuszka J., Hill M. Lipidomic analysis of plasma, erythrocytes and lipoprotein fractions of cardiovascular disease patients using UHPLC/MS, MALDI-MS and multivariate data analysis. J. Chromatogr. B. 2015;990:52–63. doi: 10.1016/j.jchromb.2015.03.010. PubMed DOI

Graessler J., Schwudke D., Schwarz P.E.H., Herzog R., Shevchenko A., Bornstein S.R. Top-down lipidomics reveals ether lipid deficiency in blood plasma of hypertensive patients. PLoS ONE. 2009;4:e6261. doi: 10.1371/journal.pone.0006261. PubMed DOI PMC

Holčapek M., Liebisch G., Ekroos K. Lipidomic Analysis. Anal. Chem. 2018;90:4249–4257. doi: 10.1021/acs.analchem.7b05395. PubMed DOI

Surma M.A., Herzog R., Vasilj A., Klose C., Christinat N., Morin-Rivron D., Simons K., Masoodi M., Sampaio J.L. An automated shotgun lipidomics platform for high throughput, comprehensive, and quantitative analysis of blood plasma intact lipids. Eur. J. Lipid Sci. Technol. 2015;117:1540–1549. doi: 10.1002/ejlt.201500145. PubMed DOI PMC

Cajka T., Fiehn O. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Analyt. Chem. 2014;61:192. doi: 10.1016/j.trac.2014.04.017. PubMed DOI PMC

Matyash V., Liebisch G., Kurzchalia T.V., Shevchenko A., Schwudke D. Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J. Lipid Res. 2008;49:1137–1146. doi: 10.1194/jlr.D700041-JLR200. PubMed DOI PMC

Schwudke D., Schuhmann K., Herzog R., Bornstein S.R., Shevchenko A. Shotgun Lipidomics on High Resolution Mass Spectrometers. Cold Spring Harb. Perspect. Biol. 2011;3:a004614. doi: 10.1101/cshperspect.a004614. PubMed DOI PMC

Pandya V., Rao A., Chaudhary K. Lipid abnormalities in kidney disease and management strategies. World J. Nephrol. 2015;4:83–91. doi: 10.5527/wjn.v4.i1.83. PubMed DOI PMC

Mikolasevic I., Žutelija M., Mavrinac V., Orlic L. Dyslipidemia in patients with chronic kidney disease: Etiology and management. Int. J. Nephrol. Renovasc. Dis. 2017;10:35–45. doi: 10.2147/IJNRD.S101808. PubMed DOI PMC

Tsimihodimos V., Dounousi E., Siamopoulos K.C. Dyslipidemia in Chronic Kidney Disease: An Approach to Pathogenesis and Treatment. Am. J. Nephrol. 2008;28:958–973. doi: 10.1159/000144024. PubMed DOI

Kim J.H., Lee S.S., Jung M.H., Yeo H.D., Kim H.J., Yang J.I., Roh G.S., Chang S.H., Park D.J. N-acetylcysteine attenuates glycerol-induced acute kidney injury by regulating MAPKs and Bcl-2 family proteins. Nephrol. Dial. Transplant. 2010;25:1435–1443. doi: 10.1093/ndt/gfp659. PubMed DOI

Homsi E., Janino P., De Faria J.B.L. Role of caspases on cell death, inflammation, and cell cycle in glycerol-induced acute renal failure. Kidney Int. 2006;69:1385–1392. doi: 10.1038/sj.ki.5000315. PubMed DOI

Soares S., Souza L.C.R., Cronin M.T., Waaga-Gasser A.M., Grossi M.F., Franco G.R., Tagliati C.A. Biomarkers and in vitro strategies for nephrotoxicity and renal disease assessment. Nephrol. Ren. Dis. 2020;5 doi: 10.15761/NRD.1000162. DOI

Shearer G.C., Carrero J.J., Heimbürger O., Barany P., Stenvinkel P. Plasma Fatty Acids in Chronic Kidney Disease: Nervonic Acid Predicts Mortality. J. Ren. Nutr. 2012;22:277–283. doi: 10.1053/j.jrn.2011.05.005. PubMed DOI

Szczuko M., Kaczkan M., Drozd A., Maciejewska D., Palma J., Owczarzak A., Marczuk N., Rutkowski P., Małgorzewicz S. Comparison of Fatty Acid Profiles in a Group of Female Patients with Chronic Kidney Diseases (CKD) and Metabolic Syndrome (MetS)–Similar Trends of Changes, Different Pathophysiology. Int. J. Mol. Sci. 2019;20:1719. doi: 10.3390/ijms20071719. PubMed DOI PMC

Varga Z., Kárpáti I., Paragh G., Buris L., Kakuk G. Relative abundance of some free fatty acids in plasma of uremic patients: Relationship between fatty acids, lipid parameters, and diseases. Nephron. 1997;77:417–421. doi: 10.1159/000190318. PubMed DOI

Ting T.C., Miyazaki-Anzai S., Masuda M., Levi M., Demer L.L., Tintut Y., Miyazaki M. Increased lipogenesis and stearate accelerate vascular calcification in calcifying vascular cells. J. Biol. Chem. 2011;286:23938–23949. doi: 10.1074/jbc.M111.237065. PubMed DOI PMC

Martin-Lorenzo M., Gonzalez-Calero L., Ramos-Barron A., Sanchez-Niño M.D., Gomez-Alamillo C., García-Segura J.M., Ortiz A., Arias M., Vivanco F., Alvarez-Llamas G. Urine metabolomics insight into acute kidney injury point to oxidative stress disruptions in energy generation and H2S availability. J. Mol. Med. 2017;95:1399–1409. doi: 10.1007/s00109-017-1594-5. PubMed DOI

Aminzadeh M.A., Vaziri N.D. Downregulation of the renal and hepatic hydrogen sulfide (H2S)-producing enzymes and capacity in chronic kidney disease. Nephrol. Dial. Transplant. 2012;27:498–504. doi: 10.1093/ndt/gfr560. PubMed DOI

Perna A.F., Luciano M.G., Ingrosso D., Pulzella P., Sepe I., Lanza D., Violetti E., Capasso R., Lombardi C., De Santo N.G. Hydrogen sulphide-generating pathways in haemodialysis patients: A study on relevant metabolites and transcriptional regulation of genes encoding for key enzymes. Nephrol. Dial. Transplant. 2009;24:3756–3763. doi: 10.1093/ndt/gfp378. PubMed DOI

Perna A.F., Luciano M.G., Ingrosso D., Raiola I., Pulzella P., Sepe I., Lanza D., Violetti E., Capasso R., Lombardi C., et al. Hydrogen sulfide, the third gaseous signaling molecule with cardiovascular properties, is decreased in hemodialysis patients. J. Ren. Nutr. 2010;20:S11–S14. doi: 10.1053/j.jrn.2010.05.004. PubMed DOI

Luczak M., Formanowicz D., Marczak Ł., Pawliczak E., Wanic-Kossowska M., Figlerowicz M., Stobiecki M. Deeper insight into chronic kidney disease-related atherosclerosis: Comparative proteomic studies of blood plasma using 2DE and mass spectrometry. J. Transl. Med. 2015;13:20. doi: 10.1186/s12967-014-0378-8. PubMed DOI PMC

Iseki K., Ikemiya Y., Iseki C., Takishita S. Proteinuria and the risk of developing end-stage renal disease. Kidney Int. 2003;63:1468–1474. doi: 10.1046/j.1523-1755.2003.00868.x. PubMed DOI

Kilpatrick R.D., McAllister C.J., Kovesdy C.P., Derose S.F., Kopple J.D., Kalantar-Zadeh K. Association between Serum Lipids and Survival in Hemodialysis Patients and Impact of Race. J. Am. Soc. Nephrol. 2007;18:293–303. doi: 10.1681/ASN.2006070795. PubMed DOI

Luczak M., Suszynska-Zajczyk J., Marczak L., Formanowicz D., Pawliczak E., Wanic-Kossowska M., Stobiecki M. Label-free quantitative proteomics reveals differences in molecular mechanism of atherosclerosis related and non-related to chronic kidney disease. Int. J. Mol. Sci. 2016;17:631. doi: 10.3390/ijms17050631. PubMed DOI PMC

Gayrard N., Ficheux A., Duranton F., Guzman C., Szwarc I., Vetromile F., Cazevieille C., Brunet P., Servel M.-F., Argilés À., et al. Consequences of increasing convection onto patient care and protein removal in hemodialysis. PLoS ONE. 2017;12:e0171179. doi: 10.1371/journal.pone.0171179. PubMed DOI PMC

Yeboah J., McNamara C., Jiang X.C., Tabas I., Herrington D.M., Burke G.L., Shea S. Association of plasma sphingomyelin levels and incident coronary heart disease events in an adult population: Multi-ethnic study of atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 2010;30:628–633. doi: 10.1161/ATVBAHA.109.199281. PubMed DOI PMC

Pongrac Barlovic D., Harjutsalo V., Sandholm N., Forsblom C., Groop P.H. Sphingomyelin and progression of renal and coronary heart disease in individuals with type 1 diabetes. Diabetologia. 2020;63:1847–1856. doi: 10.1007/s00125-020-05201-9. PubMed DOI PMC

Levey A.S., Bosch J.P., Lewis J.B., Greene T., Rogers N., Roth D. A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation. Ann. Intern. Med. 1999;130:461. doi: 10.7326/0003-4819-130-6-199903160-00002. PubMed DOI

National Clinical Guideline Centre (UK) Chronic Kidney Disease (Partial Update): Early Identification and Management of Chronic Kidney Disease in Adults in Primary and Secondary Care. National Clinical Guideline Centre; London, UK: 2014. pp. 113–120. NICE Clinical Guidelines, No. 182. PubMed

Herzog R., Schuhmann K., Schwudke D., Sampaio J.L., Bornstein S.R., Schroeder M., Shevchenko A. LipidXplorer: A software for consensual cross-platform lipidomics. PLoS ONE. 2012;7:e29851. doi: 10.1371/journal.pone.0029851. PubMed DOI PMC

Sumner L.W., Amberg A., Barrett D., Beale M.H., Beger R., Daykin C.A., Fan T.W.-M., Fiehn O., Goodacre R., Griffin J.L., et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI) Metabolomics. 2007;3:211–221. doi: 10.1007/s11306-007-0082-2. PubMed DOI PMC

Molenaar M.R., Jeucken A., Wassenaar T.A., van de Lest C.H.A., Brouwers J.F., Helms J.B. LION/web: A web-based ontology enrichment tool for lipidomic data analysis. Gigascience. 2019;8:giz061. doi: 10.1093/gigascience/giz061. PubMed DOI PMC

Cox J., Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008;26:1367–1372. doi: 10.1038/nbt.1511. PubMed DOI

Xia J., Psychogios N., Young N., Wishart D.S. MetaboAnalyst: A web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37:W652–W660. doi: 10.1093/nar/gkp356. PubMed DOI PMC

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...