Abstract
Urinary lipidomics may add new valuable biomarkers to the diagnostic armamentarium for early detection of metabolic and kidney diseases. Sources and composition of urinary lipids in healthy individuals, however, have not been investigated in detail. Shotgun lipidomics was used to quantify lipidomic profiles in native urine samples from 16 individuals (eight men, eight women) collected in five fractions over 24 h. All probands were comprehensively characterized by urinary and clinical indices. The mean total urinary lipid concentration per sample was 0.84 μM in men and 1.03 μM in women. We observed significant intra- and interindividual variations of lipid concentrations over time, but failed to detect a clear circadian pattern. Based on quantity and subclass composition it seems very unlikely that plasma serves as major source for the urinary lipidome. Considering lipid metabolites occurring in at least 20% of all samples 38 lipid species from 7 lipid classes were identified. Four phosphatidylserine and one phosphatidylethanolamine ether species (PE-O 36:5) were detectable in almost all urine samples. Sexual dimorphism has been found mainly for phosphatidylcholines and phosphatidylethanolamines. In men and in women urinary lipid species were highly correlated with urinary creatinine and albumin excretion, reflecting glomerular filtration and tubular transport processes. In women, however, lipid species deriving from urinary cells and cellular constituents of the lower genitourinary tract considerably contributed to the urinary lipidome. In conclusion, our study revealed the potential of urinary lipidomics but also the complexity of methodological challenges which have to be overcome for its implementation as a routine diagnostic tool for renal, urological and metabolic diseases.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 6 print issues and online access
$259.00 per year
only $43.17 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout





References
Pietilainen KH, Rog T, Seppanen-Laakso T, Virtue S, Gopalacharyulu P, Tang J et al. Association of lipidome remodeling in the adipocyte membrane with acquired obesity in humans. PLoS Biol 2011; 9: e1000623.
Graessler J, Bornstein TD, Goel D, Bhalla VP, Lohmann T, Wolf T et al. Lipidomic profiling before and after Roux-en-Y gastric bypass in obese patients with diabetes. Pharmacogenom J 2014; 14: 201–207.
Xia JY, Morley TS, Scherer PE . The adipokine/ceramide axis: key aspects of insulin sensitization. Biochimie 2014; 96: 130–139.
Meikle PJ, Wong G, Barlow CK, Kingwell BA . Lipidomics: potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease. Pharmacol Therap 2014; 143: 12–23.
Kulkarni H, Meikle PJ, Mamtani M, Weir JM, Barlow CK, Jowett JB et al. Plasma lipidomic profile signature of hypertension in Mexican American families: specific role of diacylglycerols. Hypertension 2013; 62: 621–626.
Graessler J, Schwudke D, Schwarz PE, Herzog R, Shevchenko A, Bornstein SR . Top-down lipidomics reveals ether lipid deficiency in blood plasma of hypertensive patients. PLoS ONE 2009; 4: e6261.
Demirkan A, Isaacs A, Ugocsai P, Liebisch G, Struchalin M, Rudan I et al. Plasma phosphatidylcholine and sphingomyelin concentrations are associated with depression and anxiety symptoms in a Dutch family-based lipidomics study. J Psychiatric Res 2013; 47: 357–362.
Gross RW, Han X . Shotgun lipidomics of neutral lipids as an enabling technology for elucidation of lipid-related diseases. Am J Physiol Endocrinol Metabolism 2009; 297: E297–E303.
Ban RH, Kamvissi V, Schulte KM, Bornstein SR, Rubino F, Graessler J . Lipidomic profiling at the interface of metabolic surgery and cardiovascular disease. Curr Atheroscler Rep 2014; 16: 455.
Quehenberger O, Armando AM, Brown AH, Milne SB, Myers DS, Merrill AH et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res 2010; 51: 3299–3305.
Sales S, Graessler J, Ciucci S, Al-Atrib R, Vihervaara T, Schuhmann K et al. Gender, contraceptives and individual metabolic predisposition shape a healthy plasma lipidome. Sci Rep 2016; 6: 27710.
Raffield LM, Hsu FC, Cox AJ, Carr JJ, Freedman BI, Bowden DW . Predictors of all-cause and cardiovascular disease mortality in type 2 diabetes: diabetes heart study. Diabetol Metab Syndr 2015; 7: 58.
Svensson MK, Cederholm J, Eliasson B, Zethelius B, Gudbjornsdottir S . Albuminuria and renal function as predictors of cardiovascular events and mortality in a general population of patients with type 2 diabetes: a nationwide observational study from the Swedish National Diabetes Register. Diab Vasc Dis Res 2013; 10: 520–529.
Dutta D, Choudhuri S, Mondal SA, Mukherjee S, Chowdhury S . Urinary albumin:creatinine ratio predicts prediabetes progression to diabetes and reversal to normoglycemia: role of associated insulin resistance, inflammatory cytokines and low vitamin D. J Diabetes 2014; 6: 316–322.
Lu Q, Tong N, Liu Y, Li N, Tang X, Zhao J et al. Community-based population data indicates the significant alterations of insulin resistance, chronic inflammation and urine ACR in IFG combined IGT group among prediabetic population. Diabetes Res Clin Pract 2009; 84: 319–324.
Thomas S, Hao L, Ricke WA, Li L . Biomarker discovery in mass spectrometry-based urinary proteomics. Proteomics Clin Appl 2016; 10: 358–370.
Konvalinka A, Scholey JW, Diamandis EP . Searching for new biomarkers of renal diseases through proteomics. Clin Chem 2012; 58: 353–365.
Jiang S, Wang Y, Liu Z . The application of urinary proteomics for the detection of biomarkers of kidney diseases. Adv Exp Med Biol 2015; 845: 151–165.
Fiseha T . Urinary biomarkers for early diabetic nephropathy in type 2 diabetic patients. Biomark Res 2015; 3: 16.
Zhao YY, Vaziri ND, Lin RC . Lipidomics: new insight into kidney disease. Adv Clin Chem 2015; 68: 153–175.
Keane WF, Tomassini JE, Neff DR . Lipid abnormalities in patients with chronic kidney disease. Contrib Nephrol 2011; 171: 135–142.
Jia L, Wang C, Zhao S, Lu X, Xu G . Metabolomic identification of potential phospholipid biomarkers for chronic glomerulonephritis by using high performance liquid chromatography-mass spectrometry. J Chromatogr B Anal Technol Biomed Life Sci 2007; 860: 134–140.
Reis A, Rudnitskaya A, Chariyavilaskul P, Dhaun N, Melville V, Goddard J et al. Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease. J Lipid Res 2015; 56: 413–422.
Yang WL, Bai Q, Li DD, A TL, Wang S, Zhao RS et al. Changes of urinary phospholipids in the chronic kidney disease patients. Biomarkers: Biochem Indicators Exposure Resp Suscept Chem 2013; 18: 601–606.
Min HK, Lim S, Chung BC, Moon MH . Shotgun lipidomics for candidate biomarkers of urinary phospholipids in prostate cancer. Anal Bioanal Chem 2011; 399: 823–830.
Kim H, Min HK, Kong G, Moon MH . Quantitative analysis of phosphatidylcholines and phosphatidylethanolamines in urine of patients with breast cancer by nanoflow liquid chromatography/tandem mass spectrometry. Anal Bioanal Chem 2009; 393: 1649–1656.
Min HK, Kong G, Moon MH . Quantitative analysis of urinary phospholipids found in patients with breast cancer by nanoflow liquid chromatography-tandem mass spectrometry: II. Negative ion mode analysis of four phospholipid classes. Anal Bioanal Chem 2010; 396: 1273–1280.
Byeon SK, Lee JY, Lee JS, Moon MH . Lipidomic profiling of plasma and urine from patients with Gaucher disease during enzyme replacement therapy by nanoflow liquid chromatography-tandem mass spectrometry. J Chromatogr A 2015; 1381: 132–139.
Herzog R, Schuhmann K, Schwudke D, Sampaio JL, Bornstein SR, Schroeder M et al. LipidXplorer: a software for consensual cross-platform lipidomics. PloS one 2012; 7: e29851.
Herzog R, Schwudke D, Schuhmann K, Sampaio JL, Bornstein SR, Schroeder M et al. A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language. Genome Biol 2011; 12: R8.
Surma MA, Herzog R, Vasilj A, Klose C, Christinat N, Morin-Rivron D et al. 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.
Rockwell HE, Gao F, Chen EY, McDaniel J, Sarangarajan R, Narain NR et al. Dynamic assessment of functional lipidomic analysis in human urine. Lipids 2016; 51: 875–886.
Ishikawa M, Maekawa K, Saito K, Senoo Y, Urata M, Murayama M et al. Plasma and serum lipidomics of healthy white adults shows characteristic profiles by subjects' gender and age. PLoS ONE 2014; 9: e91806.
Mohr NM, Harland KK, Crabb V, Mutnick R, Baumgartner D, Spinosi S et al. Urinary squamous epithelial cells do not accurately predict urine culture contamination, but may predict urinalysis performance in predicting bacteriuria. Acad Emerg Med: Off J Soc Acad Emerg Med 2016; 23: 323–330.
Vaziri ND . Lipotoxicity and impaired high density lipoprotein-mediated reverse cholesterol transport in chronic kidney disease. J Renal Nutr: Off J Council Renal Nutr Natl Kidney Found 2010; 20 (Suppl 5): S35–S43.
Kontush A, Lhomme M, Chapman MJ . Unraveling the complexities of the HDL lipidome. J Lipid Res 2013; 54: 2950–2963.
Wiesner P, Leidl K, Boettcher A, Schmitz G, Liebisch G . Lipid profiling of FPLC-separated lipoprotein fractions by electrospray ionization tandem mass spectrometry. J Lipid Res 2009; 50: 574–585.
Zhang W, Zhou X, Zhang H, Yao Q, Liu Y, Dong Z . Extracellular vesicles in diagnosis and therapy of kidney diseases. Am J Physiol Renal Physiol 2016; 311: F844–F851.
De Palma G, Sallustio F, Schena FP . Clinical application of human urinary extracellular vesicles in kidney and urologic diseases. Int J Mol Sci 2016; 17: E1043.
Fang DY, King HW, Li JY, Gleadle JM . Exosomes and the kidney: blaming the messenger. Nephrology 2013; 18: 1–10.
Gamez-Valero A, Lozano-Ramos SI, Bancu I, Lauzurica-Valdemoros R, Borras FE . Urinary extracellular vesicles as source of biomarkers in kidney diseases. Front Immunol 2015; 6: 6.
Pisitkun T, Johnstone R, Knepper MA . Discovery of urinary biomarkers. Mol Cell Proteomics 2006; 5: 1760–1771.
Gerl MJ, Sampaio JL, Urban S, Kalvodova L, Verbavatz JM, Binnington B et al. Quantitative analysis of the lipidomes of the influenza virus envelope and MDCK cell apical membrane. J Cell Biol 2012; 196: 213–221.
Leidl K, Liebisch G, Richter D, Schmitz G . Mass spectrometric analysis of lipid species of human circulating blood cells. Biochim Biophys Acta 2008; 1781: 655–664.
Magliano E, Grazioli V, Deflorio L, Leuci AI, Mattina R, Romano P et al. Gender and age-dependent etiology of community-acquired urinary tract infections. Sci World J 2012; 2012: 349597.
Gidden J, Denson J, Liyanage R, Ivey DM, Lay JO . Lipid Compositions in Escherichia coli and Bacillus subtilis during growth as determined by MALDI-TOF and TOF/TOF Mass Spectrometry. Int J Mass Spectrom 2009; 283: 178–184.
Acknowledgements
JL has been supported by grant 02.A03.21.0011 from the 5-100 Project, under the Act 211 of the Government of the Russian Federation. We declare that there is no financial support from commercial sources or other financial interests which could create a potential conflict of interest with regard to this work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no conflict of interest.
Additional information
Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website
Rights and permissions
About this article
Cite this article
Graessler, J., Mehnert, C., Schulte, KM. et al. Urinary Lipidomics: evidence for multiple sources and sexual dimorphism in healthy individuals. Pharmacogenomics J 18, 331–339 (2018). https://doi.org/10.1038/tpj.2017.24
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/tpj.2017.24