Urine and serum NMR-based metabolomics in pre-procedural prediction of contrast-induced nephropathy

  • Nooshin Dalili
  • Saeed Chashmniam
  • Seyed Mojtaba Heydari Khoormizi
  • Lida Salehi
  • Seyed Ali Jamalian
  • Mohsen Nafar
  • Shiva KalantariEmail author


Contrast induced nephropathy (CIN) has been reported to be the third foremost cause of acute renal failure. Metabolomics is a robust technique that has been used to identify potential biomarkers for the prediction of renal damage. We aim to analyze the serum and urine metabolites changes, before and after using contrast for coronary angiography, to determine if metabolomics can predict early development of CIN. 66 patients undergoing elective coronary angiography were eligible for enrollment. Urine and serum samples were collected prior to administration of CM and 72 h post procedure and analyzed by nuclear magnetic resonance. The significant differential metabolites between patients who develop CIN and patients who have stable renal function after angiography were identified using U test and receiver operating characteristic analysis was performed for each metabolite candidate. Potential susceptible pathways to cytotoxic effect of CM were investigated by pathway analysis. A predictive panel composed of six urinary metabolites had the best area under the curve. Glutamic acid, uridine diphosphate, glutamine and tyrosine were the most important serum predictive biomarkers. Several pathways related to amino acid and nicotinamide metabolism were suggested as impaired pathways in CIN prone patients. Changes exist in urine and serum metabolomics patterns in patients who do and do not develop CIN after coronary angiography hence metabolites may be potential predictive identifiers of CIN.


Contrast induced nephropathy Metabolomics Nuclear magnetic resonance Biomarker 



The authors like to thank the Chronic Kidney Disease Research Center (CKDRC) at Shahid Beheshti University of Medical Sciences for its financial support.


This work was supported by the Chronic Kidney Disease Research Center (CKDRC) and Urology and Nephrology Research Center (UNRC) at Shahid Beheshti University of Medical Sciences [462/27].

Compliance with ethical standards

Conflict of interest

There is nothing to disclose in relation to this manuscript.

Statement of human and animal rights

This study was approved by the ethical committee of the Shahid Beheshti University of Medical Sciences.

Informed consent

All patients signed an informed consent.

Supplementary material

11739_2019_2128_MOESM1_ESM.xlsx (185 kb)
Supplementary file1 (XLSX 185 kb)
11739_2019_2128_MOESM2_ESM.xlsx (194 kb)
Supplementary file2 (XLSX 193 kb)


  1. 1.
    McCullough PA (2008) Contrast-induced acute kidney injury. J Am Coll Cardiol 51(15):1419–1428Google Scholar
  2. 2.
    Zhang T, Shen L-H, Hu L-H, He B (2011) Statins for the prevention of contrast-induced nephropathy: a systematic review and meta-analysis. Am J Nephrol 33(4):344–351Google Scholar
  3. 3.
    Andreucci M, Faga T, Riccio E, Sabbatini M, Pisani A, Michael A (2016) The potential use of biomarkers in predicting contrast-induced acute kidney injury. Int J Nephrol Renovasc 9:205Google Scholar
  4. 4.
    Kellum JA, Lameire N, Aspelin P, Barsoum RS, Burdmann EA, Goldstein SL, Herzog CA, Joannidis M, Kribben A, Levey AS (2012) Kidney disease: improving global outcomes (KDIGO) acute kidney injury work group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl 2(1):1–138Google Scholar
  5. 5.
    Murphy SW, Barrett BJ, Parfrey PS (2000) Contrast nephropathy. J Am Soc Nephrol 11(1):177–182Google Scholar
  6. 6.
    Mohammed NM, Mahfouz A, Achkar K, Rafie IM, Hajar R (2013) Contrast-induced nephropathy. Heart Views 14(3):106Google Scholar
  7. 7.
    Mehran R, Nikolsky E (2006) Contrast-induced nephropathy: definition, epidemiology, and patients at risk. Kidney Int 69:S11–S15Google Scholar
  8. 8.
    Marenzi G, Cabiati A, Milazzo V, Rubino M (2012) Contrast-induced nephropathy. Intern Emerg Med 7(3):181–183. Google Scholar
  9. 9.
    Fiaccadori E, Delsante M, Fani F, Regolisti G (2018) Acute kidney injury and stroke: unresolved issues. Intern Emerg Med 13(1):13–15. Google Scholar
  10. 10.
    Zhang A, Sun H, Qiu S, Wang XJ (2013) NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis. Magn Reson Chem 51(9):549–556Google Scholar
  11. 11.
    Wang Z, Lin Y, Liang J, Huang Y, Ma C, Liu X, Yang J (2017) NMR-based metabolomic techniques identify potential urinary biomarkers for early colorectal cancer detection. Oncotarget 8(62):105819Google Scholar
  12. 12.
    Guleria A, Pratap A, Dubey D, Rawat A, Chaurasia S, Sukesh E, Phatak S, Ajmani S, Kumar U, Khetrapal CL (2016) NMR based serum metabolomics reveals a distinctive signature in patients with Lupus Nephritis. Sci Rep 6:35309Google Scholar
  13. 13.
    Kalantari S, Nafar M, Samavat S, Parvin M, Nobakht MGHBF, Barzi F (2016) 1H NMR-based metabolomics exploring urinary biomarkers correlated with proteinuria in focal segmental glomerulosclerosis: a pilot study. Magn Reson Chem 54(10):821–826Google Scholar
  14. 14.
    López-Ibáñez J, Pazos F, Chagoyen M (2016) MBROLE 2.0—functional enrichment of chemical compounds. Nucleic Acids Res 44(W1):W201–W204Google Scholar
  15. 15.
    Bachmann V, Kostiuk B, Unterweger D, Diaz-Satizabal L, Ogg S, Pukatzki S (2015) Bile salts modulate the mucin-activated type VI secretion system of pandemic Vibrio cholerae. PLoS Negl Trop Dis 9(8):e0004031Google Scholar
  16. 16.
    Abdu F, Albaik M (2016) Effect of conjugated bile salt taurodeoxycholic acid (TDCA) on mice colonic motor activity. Period Biol 118(2):99–104Google Scholar
  17. 17.
    Schaap FG, Trauner M, Jansen PL (2014) Bile acid receptors as targets for drug development. Nat Rev Gastroenterol Hepatol 11(1):55Google Scholar
  18. 18.
    Copple BL, Li T (2016) Pharmacology of bile acid receptors: evolution of bile acids from simple detergents to complex signaling molecules. Pharmacol Res 104:9–21Google Scholar
  19. 19.
    Chang S, Kim Y-H, Kim Y-J, Kim Y-W, Moon S, Lee YY, Jung JS, Kim Y, Jung H-E, Kim T-J (2018) Taurodeoxycholate increases the number of myeloid-derived suppressor cells that ameliorate sepsis in mice. Front Immunol 9:1984Google Scholar
  20. 20.
    Fimognari C, Lenzi M, Cantelli-Forti G, Hrelia P (2009) Apoptosis and modulation of cell cycle control by bile acids in human leukemia T cells. Trans N Y Acad Sci 1171(1):264–269Google Scholar
  21. 21.
    Chiang JY (2003) III. Bile acids and nuclear receptors. Am J Physiol Gastrointest Liver Physiol 284(3):G349–G356Google Scholar
  22. 22.
    Begley M, Gahan CG, Hill C (2005) The interaction between bacteria and bile. FEMS Microbiol Rev 29(4):625–651Google Scholar
  23. 23.
    Duranton F, Lundin U, Gayrard N, Mischak H, Aparicio M, Mourad G, Daurès J-P, Weinberger KM, Argilés À (2014) Plasma and urinary amino acid metabolomic profiling in patients with different levels of kidney function. Clin J Am Soc Nephrol 9(1):37–45Google Scholar
  24. 24.
    Huszar G, Elzinga M (1971) Amino acid sequence around the single 3-methylhistidine residue in rabbit skeletal muscle myosin. Biochemistry 10(2):229–236Google Scholar
  25. 25.
    Bilmazes C, Uauy R, Haverberg LN, Munro HN, Young VR (1978) Muscle protein breakdown rates in humans based on -methylhistidine (3-methylhistidine) content of mixed proteins in skeletal muscle and urinary output of -methylhistidine. Metabolism 27(5):525–530Google Scholar
  26. 26.
    Boirie Y, Albright R, Bigelow M, Nair KS (2004) Impairment of phenylalanine conversion to tyrosine inend-stage renal disease causing tyrosine deficiency. Kidney Int 66(2):591–596Google Scholar
  27. 27.
    Druml W, Roth E, Lenz K, Lochs H, Kopsa H (1989) Phenylalanine and tyrosine metabolism in renal failure: dipeptides as tyrosine source. Kidney Int Suppl 27:S282–S286Google Scholar
  28. 28.
    Diercks DB, Owen KP, Kline JA, Sutter ME (2016) Urine metabolomic analysis to detect metabolites associated with the development of contrast induced nephropathy. Clin Exp Emerg Med 3(4):204Google Scholar
  29. 29.
    Erez A, Nagamani SCS, Lee B (2011) Argininosuccinate lyase deficiency—argininosuccinic aciduria and beyond. Am J Med Genet Part C Semin Med Genet 157:45–53Google Scholar
  30. 30.
    Gérard P (2013) Metabolism of cholesterol and bile acids by the gut microbiota. Pathogens 3(1):14–24Google Scholar
  31. 31.
    Kulkarni C, Kulkarni K, Hamsa B (2005) L-Glutamic acid and glutamine: exciting molecules of clinical interest. Indian J Pharmacol 37(3):148Google Scholar
  32. 32.
    Newsholme P, Procopio J, Lima MMR, Pithon-Curi TC, Curi R (2003) Glutamine and glutamate—their central role in cell metabolism and function. Cell Biochem Funct 21(1):1–9Google Scholar
  33. 33.
    Mackenzie PI, Owens IS, Burchell B, Bock KW, Bairoch A, Belanger A, Fournel-Gigleux S, Green M, Hum DW, Iyanagi T (1997) The UDP glycosyltransferase gene superfamily: recommended nomenclature update based on evolutionary divergence. Pharmacogenetics 7(4):255–269Google Scholar
  34. 34.
    Kakehi M, Ikenaka Y, Nakayama SM, Kawai YK, Watanabe KP, Mizukawa H, Nomiyama K, Tanabe S, Ishizuka M (2015) Uridine diphosphate-glucuronosyltransferase (UGT) xenobiotic metabolizing activity and genetic evolution in Pinniped species. Toxicol Sci 147(2):360–369Google Scholar
  35. 35.
    Nair KS (2005) Amino acid and protein metabolism in chronic renal failure. J Ren Nutr 15(1):28–33Google Scholar
  36. 36.
    Garibotto G, Sofia A, Saffioti S, Bonanni A, Mannucci I, Verzola D (2010) Amino acid and protein metabolism in the human kidney and in patients with chronic kidney disease. Clin Nutr 29(4):424–433Google Scholar
  37. 37.
    Garibotto G, Pastorino N, Dertenois L (2003) Nutritional management of renal diseases. Protein and amino acid metabolism in renal disease and in renal failure. William and Wilkins, Baltimore, p 20e32Google Scholar
  38. 38.
    Hershberger KA, Martin AS, Hirschey MD (2017) Role of NAD+ and mitochondrial sirtuins in cardiac and renal diseases. Nat Rev Nephrol 13(4):213Google Scholar
  39. 39.
    Mehr AP, Parikh SM (2017) PPARγ-coactivator-1α, nicotinamide adenine dinucleotide and renal stress resistance. Nephron 137(4):253–255Google Scholar
  40. 40.
    Tran MT, Zsengeller ZK, Berg AH, Khankin EV, Bhasin MK, Kim W, Clish CB, Stillman IE, Karumanchi SA, Rhee EP, Parikh SM (2016) PGC1α drives NAD biosynthesis linking oxidative metabolism to renal protection. Nature 531(7595):528–532. Google Scholar
  41. 41.
    de Seigneux S, Martin P-Y (2017) Preventing the progression of AKI to CKD: the role of mitochondria. J Am Soc Nephrol 28(5):1327–1329Google Scholar
  42. 42.
    Wacker-Gußmann A, Bühren K, Schultheiss C, Braun SL, Page S, Saugel B, Schmid S, Mair S, Schoemig A, Schmid RM (2014) Prediction of contrast-induced nephropathy in patients with serum creatinine levels in the upper normal range by cystatin C: a prospective study in 374 patients. Am J Roentgenol 202(2):452–458Google Scholar
  43. 43.
    Bachorzewska-Gajewska H, Malyszko J, Sitniewska E, Malyszko J, Pawlak K, Mysliwiec M, Lawnicki S, Szmitkowski M, Dobrzycki S (2007) Could neutrophil-gelatinase-associated lipocalin and cystatin C predict the development of contrast-induced nephropathy after percutaneous coronary interventions in patients with stable angina and normal serum creatinine values? Kidney Blood Press Res 30(6):408–415Google Scholar
  44. 44.
    Briguori C, Visconti G, Rivera NV, Focaccio A, Golia B, Giannone R, Castaldo D, De Micco F, Ricciardelli B, Colombo A (2010) Cystatin C and contrast-induced acute kidney injury. Circulation 121(19):2117–2122Google Scholar
  45. 45.
    Connolly M, Kinnin M, McEneaney D, Menown I, Kurth M, Lamont J, Morgan N, Harbinson M (2017) Prediction of contrast induced acute kidney injury using novel biomarkers following contrast coronary angiography. QJM Int J Med 111(2):103–110Google Scholar
  46. 46.
    Tasanarong A, Hutayanon P, Piyayotai D (2013) Urinary neutrophil gelatinase-associated lipocalin predicts the severity of contrast-induced acute kidney injury in chronic kidney disease patients undergoing elective coronary procedures. BMC Nephrol 14(1):270Google Scholar
  47. 47.
    Nusca A, Miglionico M, Proscia C, Ragni L, Carassiti M, Pepe FL, Di Sciascio G (2018) Early prediction of contrast-induced acute kidney injury by a" bedside" assessment of neutrophil gelatinase-associated lipocalin during elective percutaneous coronary interventions. PLoS One 13(5):e0197833Google Scholar
  48. 48.
    Torregrosa I, Montoliu C, Urios A, Andrés-Costa MJ, Giménez-Garzó C, Juan I, Puchades MJ, Blasco ML, Carratalá A, Sanjuán R (2015) Urinary KIM-1, NGAL and L-FABP for the diagnosis of AKI in patients with acute coronary syndrome or heart failure undergoing coronary angiography. Heart Vessels 30(6):703–711Google Scholar
  49. 49.
    Nozue T, Michishita I, Mizuguchi I (2010) Predictive value of serum cystatin C, β2-microglobulin, and urinary liver-type fatty acid-binding protein on the development of contrast-induced nephropathy. Cardiovasc Interv Ther 25(2):85–90Google Scholar
  50. 50.
    He H, Li W, Qian W, Zhao X, Wang L, Yu Y, Liu J, Cheng J (2014) Urinary interleukin-18 as an early indicator to predict contrast-induced nephropathy in patients undergoing percutaneous coronary intervention. Exp Ther Med 8(4):1263–1266Google Scholar

Copyright information

© Società Italiana di Medicina Interna (SIMI) 2019

Authors and Affiliations

  • Nooshin Dalili
    • 1
  • Saeed Chashmniam
    • 2
  • Seyed Mojtaba Heydari Khoormizi
    • 3
  • Lida Salehi
    • 3
  • Seyed Ali Jamalian
    • 4
  • Mohsen Nafar
    • 3
  • Shiva Kalantari
    • 3
    Email author
  1. 1.Urology and Nephrology Research CenterShahid Beheshti University of Medical SciencesTehranIran
  2. 2.Department of ChemistrySharif University of TechnologyTehranIran
  3. 3.Chronic Kidney Disease Research CenterShahid Labbafinejad Medical Center, Shahid Beheshti University of Medical SciencesTehranIran
  4. 4.Department of CardiologyShahid Lavasani Medical CenterTehranIran

Personalised recommendations