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Identification of a urine metabolomic signature in patients with advanced-stage chronic kidney disease
Open accessAbstract
The prevalence of chronic kidney disease (CKD) is increasing and frequently progresses to end-stage renal disease. There is an urgent demand to discover novel markers of disease that allow monitoring disease progression and, eventually, response to treatment. To identify such markers, and as a proof of principle, we determined if a metabolite signature corresponding to CKD can be found in urine. In the discovery stage, we analyzed the urine metabolome by NMR of 15 patients with CKD and compared that with the metabolome of 15 healthy individuals and found a classification pattern clearly indicative of CKD. A validation cohort of urine samples from an additional 16 patients with CKD and 15 controls was then analyzed by (Selected Reaction Monitoring) liquid chromatography-triple quadrupole mass spectrometry and indicated that a group of seven urinary metabolites differed between CKD and non-CKD urine samples. This profile consisted of 5-oxoproline, glutamate, guanidoacetate, α-phenylacetylglutamine, taurine, citrate, and trimethylamine N-oxide. Thus, we identified a panel of urine metabolites differentially present in urine that may help identify and monitor patients with CKD.
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“Metabolomic analysis of urine from 31 patients identified a group of seven metabolites that differed between chronic kidney disease (CKD) and non-CKD urine samples; TMAO concentrations in plasma were higher in CKD (92).”
From Trimethylamine N -Oxide, the Microbiome, and Heart and Kidney Disease (2017) · cites this paper as [92]
Citations
Cites
Regulation of glutathione synthesis
Lu, S. C.-L. (2008) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2008.05.005 · Cited by 2,131
Selected reaction monitoring for quantitative proteomics: a tutorial
Lange, V., Picotti, P., Domon, B., & Aebersold, R. H. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.61 · Cited by 1,396
Biomarkers in chronic kidney disease: a review
Fassett, R. G., Venuthurupalli, S. K., Gobe, G. C., Coombes, J. S., Cooper, M. A., & Hoy, W. E. (2011) · Kidney International · doi:10.1038/ki.2011.198 · Cited by 466
Phisitkul, S., Khanna, A., Simoni, J. M., Broglio, K. R., Sheather, S. J., Rajab, M. H., & Wesson, D. E. (2010) · Kidney International · doi:10.1038/ki.2009.519 · Cited by 287
Bain, M. A., Faull, R., Fornasini, G., Milne, R. W., & Evans, A. M. (2006) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfk056 · Cited by 282
Metabolomics in the study of kidney diseases
Weiss, R. H., & Kim, K. (2011) · Nature Reviews Nephrology · doi:10.1038/nrneph.2011.152 · Cited by 265
Role of glutamine in human carbohydrate metabolism in kidney and other tissues
Stümvoll, M., Perriello, G., Meyer, C., & Gerich, J. E. (1999) · Kidney International · doi:10.1046/j.1523-1755.1999.055003778.x · Cited by 230
Lauridsen, M. B., Hansen, S. H., Jaroszewski, J. W., & Cornett, C. (2007) · Analytical Chemistry · doi:10.1021/ac061354x · Cited by 179
Phenylacetylglutamine May Replace Urea as a Vehicle for Waste Nitrogen Excretion
Brusilow, S. W. (1991) · Pediatric Research · doi:10.1203/00006450-199102000-00009 · Cited by 173
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Zhao, X. L., Kwan, J. Y. Y., Yip, K. W., Liu, P. P., & Liu, F. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0040-5 · Cited by 537
Trimethylamine N -Oxide, the Microbiome, and Heart and Kidney Disease
Zeisel, S. H., & Warrier, M. (2017) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-071816-064732 · Cited by 475
Zoccali, C., Vanholder, R. C., Massy, Z. A., Ortíz, A., Sarafidis, P. A., Dekker, F. W., Fliser, D., Fouque, D. P., Heine, G. H., Jager, K. J., et al. (2017) · Nature Reviews Nephrology · doi:10.1038/nrneph.2017.52 · Cited by 464
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Ding, H., Jiang, L., Xu, J., Bai, F., Zhou, Y., Yuan, Q., Luo, J., Zen, K., & Yang, J. (2017) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00036.2017 · Cited by 250
Chen, H., Cao, G., Chen, D., Wang, M., Vaziri, N. D., Zhang, Z., Mao, J., Bai, X., & Zhao, Y. (2016) · Redox Biology · doi:10.1016/j.redox.2016.09.014 · Cited by 162
Chen, D., Cao, G., Chen, H., Liu, D., Su, W. C., Yu, X.-Y., Vaziri, N. D., Liu, X., Bai, X., Zhang, L., et al. (2017) · Redox Biology · doi:10.1016/j.redox.2017.03.017 · Cited by 163
Zhang, Z., Wei, F., Vaziri, N. D., Cheng, X., Bai, X., Lin, R., & Zhao, Y. (2015) · Scientific Reports · doi:10.1038/srep14472 · Cited by 155
Zhang, Z., Vaziri, N. D., Wei, F., Cheng, X., Bai, X., & Zhao, Y. (2016) · Scientific Reports · doi:10.1038/srep22151 · Cited by 136
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Zhao, Y., Cheng, X., Vaziri, N. D., Liu, S., & Lin, R. (2014) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2014.07.019 · Cited by 130
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Nkuipou‐Kenfack, E., Duranton, F., Gayrard, N., Argilés, À., Lundin, U., Weinberger, K. M., Dakna, M., Delles, C., Mullen, W., Husi, H., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096955 · Cited by 108
Zhao, F., Bergstralh, E. J., Mehta, R. A., Vaughan, L. E., Olson, J. B., Seide, B. M., Meek, A. M., Cogal, A. G., Lieske, J. C., & Milliner, D. S. (2015) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.02810315 · Cited by 98
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Pan, L., Yu, H., Fu, J., Hu, J., Xu, H. X., Zhang, Z., Bu, M.-M., Yang, X., Zhang, H., Lu, J., et al. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.12.010 · Cited by 83
Blydt‐Hansen, T. D., Sharma, A., Gibson, I. W., Mandal, R., & Wishart, D. S. (2014) · American Journal of Transplantation · doi:10.1111/ajt.12837 · Cited by 81
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Uribarri, J. (2020) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000582 · Cited by 78
Lee, J., Lee, J., Kim, K., Lee, J., Jung, Y., Hyeon, J. S., Seo, A., Jin, W., Weon, B., Shin, N., et al. (2024) · Kidney International · doi:10.1016/j.kint.2024.01.040 · Cited by 76
Zhang, Y., Liang, F., Zhang, D., Qi, S., & Liu, Y. (2022) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2022.114046 · Cited by 77
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Rinaudo, P., Boudah, S., Junot, C., & Thévenot, E. (2016) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2016.00026 · Cited by 81
Mutter, S., Valo, E., Aittomäki, V., Nybo, K., Raivonen, L., Thorn, L. M., Forsblom, C. M., Sandholm, N., Würtz, P., & Groop, P. (2021) · Diabetologia · doi:10.1007/s00125-021-05584-3 · Cited by 73
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Cañadas‐Garre, M., Anderson, K., McGoldrick, J., Maxwell, A. P., & McKnight, A. J. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.09.020 · Cited by 66
Hsu, C., Chang‐Chien, G., Lin, S., Hou, C., Lu, P., & Tain, Y. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9020336 · Cited by 66
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Esteras, R., Pérez-Gómez, M. V., Rodríguez-Osorio, L., Ortíz, A., & Fernández‐Fernández, B. (2015) · Therapeutic Advances in Drug Safety · doi:10.1177/2042098615589905 · Cited by 50
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Zhang, X., Lei, X., Jiang, Y., Zhao, L.-M., Zou, C., Bai, Y., Li, Y., Wang, R., Li, Q., Chen, Q.-Z., et al. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01311-z · Cited by 45
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Christians, U., Klawitter, J., & Klawitter, J. (2015) · Therapeutic Drug Monitoring · doi:10.1097/ftd.0000000000000243 · Cited by 44
Ji, C., Yin, L., Mo, Y., Lu, Z., Lu, F., Lin, Q., Liu, X., Zou, C., & Wu, Y. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.780924 · Cited by 41
Liu, S., Liang, S., Liu, H., Chen, L., Sun, L., Wei, M., Jiang, H., & Wang, J. (2018) · Kidney & Blood Pressure Research · doi:10.1159/000489912 · Cited by 41
Martin‐Lorenzo, M., Gonzalez‐Calero, L., Ramos-Barron, M. A., Sánchez-Niño, M. D., Gómez-Alamillo, C., García-Segura, J. M., Ortíz, A., Arias, M., Vivanco, F., & Álvarez‐Llamas, G. (2017) · Journal of Molecular Medicine · doi:10.1007/s00109-017-1594-5 · Cited by 41
Self-citationFerlizza, E., Isani, G., Dondi, F., Andreani, G., Vasylyeva, K., Bellei, E., de Almeida, A. M., & Matzapetákis, M. (2020) · Journal of Proteomics · doi:10.1016/j.jprot.2020.103795 · Cited by 41
Liu, C., Wang, X., Wang, X., Zhang, Y., Min, W., Yu, P., Miao, J., Shen, W., Chen, S., Zhou, S. Y., et al. (2022) · Theranostics · doi:10.7150/thno.78376 · Cited by 42
Martin‐Lorenzo, M., Gonzalez‐Calero, L., Maroto, A. S., Martínez, P. J., Zubiri, I., de la Cuesta, F., Mouriño-Álvarez, L., Barderas, M. G., Heredero, A., Aldámiz‐Echevarría, G., et al. (2015) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2015.12.006 · Cited by 41
Self-citationBiomarkers of obstructive nephropathy using a metabolomics approach in rat
Zhang, Z., He, J., Qin, W., Zhao, Y., & Tan, N. (2018) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2018.10.004 · Cited by 39
Hanifa, M. A., Skott, M., Maltesen, R. G., Rasmussen, B. S., Nielsen, S. U., Frøkiær, J., Ring, T., & Wimmer, R. (2019) · Metabolomics · doi:10.1007/s11306-019-1569-3 · Cited by 38
Boizard, F., Brunchault, V., Moulos, P., Breuil, B., Klein, J., Lounis, N., Caubet, C., Tellier, S., Bascands, J., Decramer, S., et al. (2016) · Scientific Reports · doi:10.1038/srep34453 · Cited by 38
Gonzalez‐Calero, L., Martin‐Lorenzo, M., Martínez, P. J., Martin, M. B., Ruiz‐Hurtado, G., Segura, J., de la Cuesta, F., Barderas, M. G., Ruilope, L. M., Vivanco, F., et al. (2016) · Translational research · doi:10.1016/j.trsl.2016.07.003 · Cited by 37
Self-citationLiu, Y., Yu, Q., Ge, Y., Zhang, W.-S., Fan, Y., Ma, C., Liu, Q., & Qi, L. (2016) · Scientific Reports · doi:10.1038/srep39045 · Cited by 37
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Cid, S. B., Sánchez‐Ortega, A., Unceta, N., Andrade, F., Aldamiz-Echevarria, L. J., Goicolea, M. A., & Barrio, R. J. (2018) · The Analyst · doi:10.1039/c8an00864g · Cited by 37
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Tan, Y. M., Gao, Y., Teo, G., Koh, H. W. L., Tai, E. S., Khoo, C. M., Choi, K. P., Zhou, L., & Choi, H. (2021) · Metabolites · doi:10.3390/metabo11040228 · Cited by 37
Boelaert, J., Lynen, F., Glorieux, G., Schepers, E., Neirynck, N., & Vanholder, R. C. (2017) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-016-0165-x · Cited by 35
Lee, J., Choi, J., Kwon, Y., Lee, D., Jung, H., Ryu, H., Cho, J., Ryu, D. H., Kim, Y.-L., & Hwang, G. (2016) · Clinica Chimica Acta · doi:10.1016/j.cca.2016.05.018 · Cited by 35
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Øvrehus, M. A., Bruheim, P., Ju, W., Zelnick, L. R., Langlo, K. A. R., Sharma, K., de Boer, I. H., & Hallan, S. I. (2018) · Kidney International Reports · doi:10.1016/j.ekir.2018.10.007 · Cited by 34
Perales-Quintana, M. M., Saucedo, A. L., Lucio-Gutiérrez, J. R., de Torres, N. H. W., Alarcón-Galván, G., Govea-Torres, G., Sánchez‐Martínez, C., Pérez‐Rodríguez, E., Garza, F., & Cordero‐Pérez, P. (2019) · PeerJ · doi:10.7717/peerj.7113 · Cited by 34
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Öman, T., Tessem, M., Bathen, T. F., Bertilsson, H., Angelsen, A., Hedenström, M., & Andreassen, T. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0413-z · Cited by 34
Gonzalez‐Calero, L., Martin‐Lorenzo, M., Ramos-Barron, M. A., Ruiz-Criado, J., Maroto, A. S., Ortíz, A., Gómez-Alamillo, C., Arias, M., Vivanco, F., & Álvarez‐Llamas, G. (2016) · Scientific Reports · doi:10.1038/srep19667 · Cited by 35
Self-citationSong, Y., Wei, H., Zhou, Z., Wang, H., Hang, W., Wu, J., & Wang, D. W. (2024) · Frontiers of Medicine · doi:10.1007/s11684-024-1055-9 · Cited by 38
Agudiez, M., Martínez, P. J., Martin‐Lorenzo, M., Heredero, A., Santiago‐Hernandez, A., Molero, D., García-Segura, J. M., Aldámiz‐Echevarría, G., & Álvarez‐Llamas, G. (2020) · BMC Biology · doi:10.1186/s12915-020-00924-y · Cited by 32
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Kalantari, S., & Nafar, M. (2019) · Biomarkers in Medicine · doi:10.2217/bmm-2019-0008 · Cited by 32
Gonzalez‐Calero, L., Martin‐Lorenzo, M., de la Cuesta, F., Maroto, A. S., Martin, M. B., Ruiz‐Hurtado, G., Pulido-Olmo, H., Segura, J., Barderas, M. G., Ruilope, L. M., et al. (2016) · Cardiovascular Diabetology · doi:10.1186/s12933-016-0331-7 · Cited by 30
Self-citationKwon, S., Hyeon, J. S., Jung, Y., Li, L., An, J. N., Kim, Y. C., Yang, S. H., Kim, T., Kim, D. K., Lim, C. S., et al. (2023) · Kidney Research and Clinical Practice · doi:10.23876/j.krcp.22.152 · Cited by 28
Guo, F., Dai, Q., Zeng, X., Liu, Y., Tan, Z., Zhang, H., & Ouyang, D. (2020) · International Urology and Nephrology · doi:10.1007/s11255-020-02632-6 · Cited by 27
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Luck, M. M., Bertho, G., Bateson, M., Karras, A., Yartseva, A., Thervet, É., Damon, C., & Pallet, N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0166905 · Cited by 24
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Dabbousy, R., Rima, M., Roufayel, R., Rahal, M. K., Legros, C., Sabatier, J., & Fajloun, Z. (2024) · Pharmaceuticals · doi:10.3390/ph17101307 · Cited by 23
Non‐invasive staging of chronic kidney allograft damage using urine metabolomic profiling
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Banas, M., Neumann, S., Eiglsperger, J., Schiffer, E., Putz, F. J., Reichelt‐Wurm, S., Krämer, B. K., Pagel, P., & Banas, B. (2018) · Metabolomics · doi:10.1007/s11306-018-1419-8 · Cited by 23
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Kuleš, J., Rubić, I., Ljubić, B. B., Bilić, P., Rafaj, R. B., Brkljačić, M., Burchmore, R., Eckersall, P. D., & Mrljak, V. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.715701 · Cited by 22
Banerjee, S., Wong, A. C.-Y., Yan, X., Wu, B., Zhao, H., Tibshirani, R. J., Zare, R. N., & Brooks, J. D. (2019) · Scientific Reports · doi:10.1038/s41598-019-47396-x · Cited by 21
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Das, S., Rajeswari, V. D., Venkatraman, G., Elumalai, R., Sivaraman, D., & Ramanathan, G. (2023) · Translational research · doi:10.1016/j.trsl.2023.11.002 · Cited by 22
Chachaj, A., Matkowski, R., Gröbner, G., Szuba, A., & Dudka, I. (2020) · Diagnostics · doi:10.3390/diagnostics10110936 · Cited by 22
Pan, S., Zhao, D., Duan, S., & Chen, X. (2023) · International Urology and Nephrology · doi:10.1007/s11255-023-03500-9 · Cited by 22
Weinert, C. H., Empl, M. T., Krüger, R., Frommherz, L., Egert, B., Steinberg, P., & Kulling, S. E. (2016) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201600651 · Cited by 19
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Kordalewska, M., Macioszek, S., Wawrzyniak, R., Sikorska-Wiśniewska, M., Śledziński, T., Chmielewski, M. S., Mika, A., & Markuszewski, M. J. (2019) · Journal of Chromatography B · doi:10.1016/j.jchromb.2019.04.003 · Cited by 19
Taherkhani, A., Nafar, M., Oskouie, A. A., Broumandnia, N., Parvin, M., Mahmoudieh, L., & Kalantari, S. (2019) · Archives of Medical Research · doi:10.1016/j.arcmed.2019.08.004 · Cited by 19
Stryjak, I., Warmuzińska, N., Bogusiewicz, J., Łuczykowski, K., & Bojko, B. (2020) · Journal of Separation Science · doi:10.1002/jssc.202000032 · Cited by 18
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Gil, A. M., de Pinho, P. G., Monteiro, M. S., & Duarte, I. F. (2015) · Bioanalysis · doi:10.4155/bio.15.167 · Cited by 17
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Metadata
- Authors
- Posada-Ayala, M., Zubiri, I., Martin-Lorenzo, M., Sanz-Maroto, A., Molero, D., Gonzalez-Calero, L., Fernandez-Fernandez, B., de la Cuesta, F., Laborde, C. M., Barderas, M. G., Ortiz, A., Vivanco, F., & Alvarez-Llamas, G.
- Affiliations
- Instituto de Investigación Sanitaria Fundación Jiménez Díaz (ES)
- Universidad Complutense de Madrid (ES)
- Servicio de Salud de Castilla La Mancha (ES)
- Venue
- Kidney International
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 85
- Issue
- 1
- Pages
- 103-111
- ISSN
- 0085-2538 (ISSN-L)
- OpenAlex
- W2053835433
- Cited by
- 167
- References
- 22
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 10 · 2023: 13 · 2022: 14 · 2021: 11 · 2020: 24 · 2019: 15 · 2018: 14 · 2017: 15 · 2016: 16 · 2015: 10 · 2014: 10
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Kidney International (publishedVersion)
- PubMed (publishedVersion)
- Hispana (submittedVersion)
- Biblos-e Archivo (Universidad Autónoma de Madrid) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Keywords
- chronic kidney disease, urine metabolomics, metabolomic signatures, biomarkers, disease progression monitoring, pyroglutamic acid, glutamate, taurine, citrate, trimethylamine N-oxide, NMR metabolomics, multiple reaction monitoring
- MeSH terms
- Adult, Aged, Aged, 80 and over, Female, Humans, Kidney Failure, Chronic, Male, Middle Aged, Mass Spectrometry, Cohort Studies, Biomarkers, Case-Control Studies, Metabolome
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