Serum Metabolite Concentrations and Decreased GFR in the General Population
Open accessAbstract
Background Metabolites such as creatinine and urea are established kidney function markers. High-throughput metabolomic studies have not been reported in large general population samples spanning normal kidney function and chronic kidney disease (CKD).Study design Cross-sectional observational studies of the general population.Setting and participants 2 independent samples: KORA F4 (discovery sample, n = 3,011) and Twins UK (validation sample, n = 984). EXPOSURE FACTORS: 151 serum metabolites, quantified by targeted mass spectrometry.Outcomes and measurements Metabolites and their 22,650 ratios were analyzed by multivariable-adjusted linear regression for their association with glomerular filtration rate (eGFR), estimated separately from creatinine and cystatin C levels by CKD-EPI (CKD Epidemiology Collaboration) equations. After correction for multiple testing, significant metabolites (P < 3.3 × 10(-4) for single metabolites; P < 2.2 × 10(-6) for ratios) were meta-analyzed with independent data from the TwinsUK Study.Results Replicated associations with eGFR were observed for 22 metabolites and 516 metabolite ratios. Pooled P values ranged from 7.1 × 10(-7) to 1.8 × 10(-69) for the replicated single metabolites. Acylcarnitines such as glutarylcarnitine were associated inversely with eGFR (-3.73 mL/min/1.73 m(2) per standard deviation [SD] increase, pooled P = 1.8 × 10(-69)). The replicated ratio with the strongest association was the ratio of serine to glutarylcarnitine (P = 3.6 × 10(-81)). Almost all replicated phenotypes associated with decreased eGFR (<60 mL/min/1.73 m(2); n = 172 cases) in KORA F4: per 1-SD increment, ORs ranged from 0.29-2.06. Across categories of a metabolic score consisting of 3 uncorrelated metabolites, the prevalence of decreased eGFR increased from 3% to 53%.Limitations Cross-sectional study design, GFR was estimated, limited number of metabolites.Conclusions Distinct metabolic phenotypes were reproducibly associated with eGFR in 2 separate population studies. They may provide novel insights into renal metabolite handling, improve understanding of pathophysiology, or aid in the diagnosis of kidney disease. Longitudinal studies are needed to clarify whether changes in metabolic phenotypes precede or result from kidney function impairment.
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Numerous plasma metabolites have been associated with longitudinal eGFR decline or cross-sectional eGFR decline in the general population.
“In addition, numerous plasma metabolites have been associated with longitudinal eGFR183 or cross-sectional eGFR decline230 in the general population.”
From The gut microbiota and the brain–gut–kidney axis in hypertension and chronic kidney disease (2018) · cites this paper as [230]
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“Other cross-sectional studies have also analyzed the relationship between GFR and metabolites in the normal population (Sekula et al. 2016; Goek et al. 2012).”
BackgroundGenome-Wide Association Studies of Metabolite Concentrations (mGWAS): Relevance for Nephrology
Köttgen, A., Raffler, J., Sekula, P., & Kastenmüller, G. (2018) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2018.01.009 · Cited by 37 · see citing works
Self-citationWang, F., Sun, L., Sun, Q., Liang, L., Gao, X., Li, R., Pan, A., Li, H., Deng, Y., Hu, F. B., et al. (2018) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.07650717 · Cited by 36 · see citing works
“Age, yr 5866 5866 6166 Male 745 (42) 631 (42) 114 (42) Beijing resident 721 (41) 608 (41) 113 (41) Urban resident 725 (41) 614 (41) 111 (41) Education level, yr 0–6 828 (47) 686 (46) 142 (52) 7–9 604 (34) 528 (35) 76 (28) $10 333 (19) 277 (19) 56 (20) Current smoker 484 (27) 406 (27) 78 (28) Current drinker 436 (25) 3…”
Esmati, P., Najjar, N., Emamgholipour, S., Hosseinkhani, S., Arjmand, B., Soleimani, A., Kakaei, A., & Razi, F. (2021) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-021-00786-3 · Cited by 35 · see citing works
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 · see citing works
Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage
Gagnebin, Y., Boccard, J., Ponte, B., & Rudaz, S. L. (2018) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2018.08.046 · Cited by 35 · see citing works
Metabolomics insights into pathophysiological mechanisms of nephrology
Zhang, A., Sun, H. J., Qiu, S., & Wang, X. (2013) · International Urology and Nephrology · doi:10.1007/s11255-013-0600-2 · Cited by 33 · see citing works
“Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100 %.”
BackgroundInfluentialA Systems-Level View of Renal Metabolomics
Rhee, E. P. (2018) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2018.01.005 · Cited by 31 · see citing works
Wiesenhofer, F. M., Herzog, R., Boehm, M., Wagner, A., Unterwurzacher, M., Kasper, D. C., Alper, S. L., Vychytil, A., Aufricht, C., & Kratochwill, K. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2018.01961 · Cited by 31 · see citing works
“In our cohort of PD effluent samples we found seven short and mediumchain acylcarnitines significantly elevated in association with anuria and prolonged time on PD, all of which were previously correlated with eGFR decline in a large cross-sectional study (Goek et al., 2012).”
ResultTitan, S., Venturini, G., Padilha, K., Goulart, A. C., Lotufo, P. A., Benseñor, I. M., Krieger, J. E., Thadhani, R., Rhee, E. P., & Pereira, A. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213764 · Cited by 29 · see citing works
Optimization of kidney dysfunction prediction in diabetic kidney disease using targeted metabolomics
Ibarra‐González, I., Cruz‐Bautista, I., Bello‐Chavolla, O. Y., Vela-Amieva, M. B., Pallares-Méndez, R., Nevarez, D. R. D. S. Y., Salas-Tapia, M. F., Rosas-Flota, X., González-Acevedo, M., Palacios-Peñaloza, A., et al. (2018) · Acta Diabetologica · doi:10.1007/s00592-018-1213-0 · Cited by 29 · see citing works
Machine learning distilled metabolite biomarkers for early stage renal injury
Guo, Y. P., Yu, H., Chen, D., & Zhao, Y. (2019) · Metabolomics · doi:10.1007/s11306-019-1624-0 · Cited by 28 · see citing works
Fest, J., Vijfhuizen, L. S., Goeman, J. J., Veth, O., Joensuu, A., Perola, M., Männistö, S., Ness‐Jensen, E., Hveem, K., Haller, T., et al. (2019) · Endocrinology · doi:10.1210/en.2019-00165 · Cited by 27 · see citing works
Associations between thyroid hormones and serum metabolite profiles in an euthyroid population
Jourdan, C., Linseisen, J., Meisinger, C., Petersen, A.-K., Gieger, C., Rawal, R., Illig, T., Heier, M., Peters, A., Wallaschofski, H., et al. (2013) · Metabolomics · doi:10.1007/s11306-013-0563-4 · Cited by 26 · see citing works
“2011), cystatin C (Goek et al. 2012), and creatinine (Seissler et al.”
BackgroundMethodSelf-citationEpidemiology research to foster improvement in chronic kidney disease care
Luo, S., & Grams, M. E. (2019) · Kidney International · doi:10.1016/j.kint.2019.11.010 · Cited by 26 · see citing works
Biomarkers of chronic kidney disease in older individuals: navigating complexity in diagnosis
Muglia, L., Dio, M. D., Filicetti, E., Greco, G. I., Volpentesta, M., Beccacece, A., Fabbietti, P., Lattanzio, F., Corsonello, A., Gembillo, G., et al. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1397160 · Cited by 31 · see citing works
Diémé, B., Halimi, J., Emond, P., Büchler, M., Nadal‐Desbarats, L., Blasco, H., & Guellec, C. B. (2014) · Transplantation · doi:10.1097/tp.0000000000000039 · Cited by 24 · see citing works
Plasma acylcarnitines could predict prognosis and evaluate treatment of IgA nephropathy
Xia, F.-Y., Zhu, L., Xu, C., Wu, Q., Chen, W.-J., Zeng, R., & Deng, Y. (2019) · Nutrition & Metabolism · doi:10.1186/s12986-018-0328-1 · Cited by 23 · see citing works
“[14], 12 of which were measured in our study.”
BackgroundResultl-Carnitine status in end-stage renal disease patients on automated peritoneal dialysis
Liberato, L. D., Arduini, A., Rossi, C., Castelnuovo, A. F. D., Posari, C., Sacchetta, P., Urbani, A., & Bonomini, M. (2014) · Journal of Nephrology · doi:10.1007/s40620-014-0076-x · Cited by 23 · see citing works
“ples from two different populations found, amongst other things, that serum glutarylcarnitine levels were inversely correlated with estimated glomerular filtration rate (eGFR) [26].”
BackgroundA metabolomic investigation of serum perfluorooctane sulfonate and perfluorooctanoate
Rhee, J., Loftfield, E., Albanes, D., Layne, T. M., Stolzenberg‐Solomon, R. Z., Liao, L. M., Playdon, M. C., Berndt, S. I., Sampson, J. N., Freedman, N. D., et al. (2023) · Environment International · doi:10.1016/j.envint.2023.108198 · Cited by 22 · see citing works
Multiplatform metabolomics provides insight into the molecular basis of chronic kidney disease
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 · see citing works
Endothelin-1 Overexpression Improves Renal Function in eNOS Knockout Mice
Tsuprykov, O., Chaykovska, L., Kretschmer, A., Stasch, J., Pfab, T., Relle, K., Reichetzeder, C., Kalk, P., Adamski, J., & Hocher, B. (2015) · Cellular Physiology and Biochemistry · doi:10.1159/000438516 · Cited by 18 · see citing works
Self-citationAssociation Between Newborn Metabolic Profiles and Pediatric Kidney Disease
Sood, M. M., Murphy, M. S. Q., Hawken, S., Wong, C. A., Potter, B. K., Burns, K. D., Tsampalieros, A. K., Atkinson, K. M., Chakraborty, P. K., & Wilson, K. R. (2018) · Kidney International Reports · doi:10.1016/j.ekir.2018.02.001 · Cited by 18 · see citing works
“696 C16, and C16:1OH have been previously reported to be strongly associated with a decline in estimated glomerular filtration rates.(30) Finally, production of the endocrine marker 17-hydroxyprogesterone was strongly associated with CKD and dialysis in our models.”
BackgroundFamilial Resemblance for Serum Metabolite Concentrations
Draisma, H. H. M., Beekman, M., Pool, R., van Ommen, G. B., Vaarhorst, A. A. M., de Craen, A. J. M., Willemsen, G. A. H. M., Slagboom, P. E., & Boomsma, D. I. (2013) · Twin Research and Human Genetics · doi:10.1017/thg.2013.59 · Cited by 18 · see citing works
“p150 kit allows for the quantification of hexose, which is 90–95% glucose (Goek et al., 2012) and this is an important source of energy for the body.”
BackgroundMethodInfluentialZhang, F., Wu, C., Jia, C., Gao, K., Wang, J., Zhao, H., Wang, W., & Chen, M. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.03.011 · Cited by 17 · see citing works
Oxidative stress increases 1-deoxysphingolipid levels in chronic kidney disease
Gui, T., Li, Y., Zhang, S., Alecu, I., Chen, Q., Zhao, Y., Hornemann, T., Kullak-Ublick, G. A., & Gai, Z. (2021) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2021.01.011 · Cited by 17 · see citing works
Twin metabolomics: the key to unlocking complex phenotypes in nutrition research
Barron, R. E., Bermingham, K. M., Brennan, L., Gibney, E. R., Gibney, M. J., Ryan, M. F., & O’Sullivan, A. M. (2016) · Nutrition Research · doi:10.1016/j.nutres.2016.01.010 · Cited by 17 · see citing works
Wruck, W., Boima, V., Erichsen, L., Thimm, C., Koranteng, T., Kwakyi, E. P. K., Antwi, S., Adu, D., & Adjaye, J. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010038 · Cited by 16 · see citing works
“Biomarkers for the early diagnosis of CKD are urgently needed and studies aiming at their identification have been performed using serum [13] as well as urine samples [14,15].”
BackgroundRevealing metabolic pathways relevant to prediabetes based on metabolomics profiling analysis
Ouyang, Y., Jin, Y., Zhao, X., Chen, M., Yang, P., Zheng, X., Zeng, L., Chen, L., & Tian, Z. (2020) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2020.09.016 · Cited by 16 · see citing works
Irreversible post-translational modifications – Emerging cardiovascular risk factors
Wu, Z., Jankowski, V., & Jankowski, J. (2021) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2021.101010 · Cited by 16 · see citing works
Novel Filtration Markers for GFR Estimation.
Karger, A. B., Inker, L. A., Coresh, J., Levey, A. S., & Eckfeldt, J. H. (2017) · Europe PMC (PubMed Central) · Cited by 15 · see citing works
Mullen, W., Saigusa, D., Abe, T., Adamski, J., & Mischak, H. (2014) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2014.02.009 · Cited by 13 · see citing works
Self-citationHicks, L., Ralphs, S. J. L., & Williams, H. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2377-9_16 · Cited by 15 · see citing works
The Role of Metabolomics in the Study of Kidney Diseases and in the Development of Diagnostic Tools
Christians, U., Albuisson, J., Klawitter, J., & Klawitter, J. (2010) · Elsevier eBooks · doi:10.1016/b978-0-12-375672-5.10003-9 · Cited by 13 · see citing works
Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience
Downie, M. L., Desjarlais, A., Verdin, N., Woodlock, T., & Collister, D. (2023) · Canadian Journal of Kidney Health and Disease · doi:10.1177/20543581231209012 · Cited by 14 · see citing works
Metabolome-wide association study of estimated glomerular filtration rates in Hispanics
Lin, B. M., Zhang, Y., Yu, B., Boerwinkle, E., Thygarajan, B., Yunes, M., Daviglus, M. L., Qi, Q., Kaplan, R. C., Lash, J. P., et al. (2021) · Kidney International · doi:10.1016/j.kint.2021.09.032 · Cited by 10 · see citing works
Cardiovascular risk factors, living and ageing in Halle: the CARLA study
Hassan, L., Efremov, L., Großkopf, A., Kartschmit, N., Medenwald, D., Schott, A., Schmidt‐Pokrzywniak, A., Lacruz, M. E., Tiller, D., Kraus, F. B., et al. (2022) · European Journal of Epidemiology · doi:10.1007/s10654-021-00824-7 · Cited by 11 · see citing works
“The sample preparation and mass spectrometric measurements, as well as the metabolite nomenclature, have been described previously [49, 50].”
MethodRhee, E. P., Clish, C. B., Pierce, K. A., Saad, A., Lerman, L. O., & Textor, S. C. (2014) · Journal of Hypertension · doi:10.1097/hjh.0000000000000470 · Cited by 10 · see citing works
Hassan, M. H., Galal, O., Sakhr, H. M., Kamaleldeen, E. B., Zekry, N. F., Fateen, E., & Toghan, R. (2023) · Biomedical Chromatography · doi:10.1002/bmc.5747 · Cited by 9 · see citing works
Takaya, H., Namisaki, T., Kitade, M., Shimozato, N., Kaji, K., Tsuji, Y., Nakanishi, K., Noguchi, R., Fujinaga, Y., Sawada, Y., et al. (2019) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v11.i10.887 · Cited by 9 · see citing works
Tremblay‐Gravel, M., Fortier, A., Baron, C., David, C., Mehanna, P., Ducharme, A., Hussin, J., Hu, Q., Tardif, J., Rosiers, C. D., et al. (2021) · Metabolites · doi:10.3390/metabo11040196 · Cited by 9 · see citing works
“They were then compared using stepwise analysis of covariance (ANCOVA) multivariable models considering preselected variables known to affect circulating levels of lipid metabolites [34,35].”
Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses
Schultheiß, U. T., Kosch, R., Kotsis, F., Altenbuchinger, M., & Zacharias, H. U. (2021) · Metabolites · doi:10.3390/metabo11070460 · Cited by 8 · see citing works
“The authors compared a subset of the CRIC Study population with rapid progression of kidney disease according to eGFR slopes to a subset with slow progression.”
BackgroundMethodInfluentialLi, X., Miao, Y., Fang, Z., & Zhang, Q. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01058-1 · Cited by 8 · see citing works
Circulating metabolites improve the prediction of renal impairment in patients with type 2 diabetes
Trischitta, V., Mastroianno, M., Scarale, M. G., Prehn, C. P., Salvemini, L., Fontana, A., Adamski, J., Schena, F. P., Cosmo, S. D., Copetti, M., et al. (2023) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2023-003422 · Cited by 9 · see citing works
Self-citationWarmuzińska, N., Łuczykowski, K., Stryjak, I., Wojtal, E., Woderska, A., Masztalerz, M., Włodarczyk, Z., & Bojko, B. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252413502 · Cited by 8 · see citing works
Renal insufficiency mimicking glutaric acidemia type 1 on newborn screening
Matsumoto, M., Awano, H., Bo, R., Nagai, M., Tomioka, K., Nishiyama, M., Ninchouji, T., Nagase, H., Yagi, M., Morioka, I., et al. (2017) · Pediatrics International · doi:10.1111/ped.13438 · Cited by 7 · see citing works
van der Burgh, A. C., Geurts, S., Ahmad, S., Ikram, M. A., Chaker, L., Ferraro, P. M., & Ghanbari, M. (2023) · Clinical Kidney Journal · doi:10.1093/ckj/sfad286 · Cited by 7 · see citing works
“…other studies have also shown that circulating metabolites of the tryptophan pathway were associated with kidney function and disease [24 ,26 ,29 ],which is in line with our findings demonstrating a strong association of C-glycosyltryptophan with eGFRcys and CKD. Notably,…”
ResultEphraim, E., & Jewell, D. E. (2023) · Frontiers in Bioscience-Elite · doi:10.31083/j.fbe1502008 · Cited by 7 · see citing works
“Lower levels of carnitines may also be beneficial since glutarylcarnitine is inversely associated with eGFR in humans [ 43] and is at higher levels in plasma from people with diabetic kidney disease compared with those with type 2 diabetes alone [44].”
BackgroundLi, T., Grams, M. E., Inker, L. A., Chen, J., Rhee, E. P., Warady, B. A., Levey, A. S., Denburg, M., Furth, S. L., Vasan, R. S., et al. (2024) · Clinical Kidney Journal · doi:10.1093/ckj/sfae108 · Cited by 6 · see citing works
“Previous studies identified a handful of metabolites reliably associated with estimated GFR [45 ], but to our knowledge, the present study is the first to rigorously assess metabolite correlations with mGFR across a range of patient and marker characteristics in studies that include children and…”
ResultClinical and Metabolic Signatures of FAM47E–SHROOM3 Haplotypes in a General Population Sample
Ghasemi-Semeskandeh, D., König, E., Foco, L., Đorđević, N., Gögele, M., Rainer, J., Ralser, M., Acoba, D., Domingues, F. S., Peters, D. J. M., et al. (2025) · Kidney International Reports · doi:10.1016/j.ekir.2025.02.018 · Cited by 6 · see citing works
Leiherer, A., Muendlein, A., Rein, P., Saely, C. H., Kinz, E., Vonbank, A., Fraunberger, P., & Drexel, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114240 · Cited by 6 · see citing works
“Respective DNA from patient blood samples has been extracted using the chemagen magnetic separation module I (PerkinElmar Inc., Baesweiler, Germany) by magnetic beads based method from 2ml whole blood following the manufacturers manual and quantification of extracted nucleotides has been checked spectrophotometrically…”
BackgroundInfluentialYang, X., Peng, J., Sun, F., Wu, P., Wang, Y., Xu, Y., Lai, Y., Yuan, J., Zhang, S., Jiang, L., et al. (2024) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae881 · Cited by 6 · see citing works
Analysis of Purine Metabolites Maternal Serum for Evaluating the Risk of Gestosis
Senyavina, N. V., Анатольевна, Х. С., Grebennik, T. K., & Pavlovich, S. V. (2013) · Bulletin of Experimental Biology and Medicine · doi:10.1007/s10517-013-2225-y · Cited by 6 · see citing works
An, T.-F., Zhang, Z.-P., Xue, J.-T., Luo, W.-M., Li, Y., Fang, Z., & Zong, G.-W. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1279034 · Cited by 5 · see citing works
Metabolic Signature of CKD: The Search Continues
Mathew, A. V., & Pennathur, S. (2012) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2012.05.005 · Cited by 5 · see citing works
Towards Metabolic Biomarkers for the Diagnosis and Prognosis of CKD
Lundin, U., & Weinberger, K. M. (2018) · InTech eBooks · doi:10.5772/intechopen.80335 · Cited by 4 · see citing works
“In a large independent study, the kynurenine-to-tryptophan ratio was one of the strongest predictors of changes in kidney function between consecutive visits of study subjects in the Cooperative Health Research in the Region of Augsburg (KORA) S4 and F4 surveys and also of newly diagnosed CKD in this roughly 7-year pe…”
BackgroundInfluentialEstimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function
Verissimo, T., Faivre, A., Sgardello, S. D., Naesens, M., de Seigneux, S., Criton, G., & Legouis, D. (2022) · Metabolites · doi:10.3390/metabo12010057 · Cited by 4 · see citing works
Application of Metabolomics to Renal and Cardiometabolic Diseases
Rebholz, C. M., & Rhee, E. P. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0239-3_20 · Cited by 4 · see citing works
Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease
Nusinovici, S., Li, H., Chong, C. C. Y., Yu, M., Sørensen, I. M. H., Bisgaard, L. S., Christoffersen, C., Bro, S., Liu, S., Liu, J., et al. (2024) · Journal of Nephrology · doi:10.1007/s40620-023-01872-w · Cited by 3 · see citing works
Targeted Metabolomics: The Next Generation of Clinical Chemistry!
Weinberger, K. M., & Breit, M. (2016) · Translational bioinformatics · doi:10.1007/978-94-017-7543-4_7 · Cited by 3 · see citing works
Al-Majdoub, M., Herzog, K., Daka, B., Magnusson, M., Råstam, L., Lindblad, U., & Spégel, P. (2018) · Metabolites · doi:10.3390/metabo8040078 · Cited by 3 · see citing works
“Our results confirm previous studies, which indicated higher acylcarnitine levels in CKD [17] and an inverse association with eGFR [18], which was suggested to be caused by impaired excretory function in the failing kidney [19].”
Result
- Authors
- Goek, O.-N., Döring, A., Gieger, C., Heier, M., Koenig, W., Prehn, C., Römisch-Margl, W., Wang-Sattler, R., Illig, T., Suhre, K., Sekula, P., Zhai, G., Adamski, J., Köttgen, A., & Meisinger, C.
- Affiliations
- University Medical Center Freiburg (DE)
- Helmholtz Munich (DE)
- Memorial University of Newfoundland (CA)
- Johns Hopkins University (US)
- Corresponding author
- Köttgen, Anna
- ORCID
- Venue
- American Journal of Kidney Diseases
- Publisher
- Elsevier BV
- Published
- 2012-08
- Volume
- 60
- Issue
- 2
- Pages
- 197-206
- ISSN
- 0272-6386 (ISSN-L)
- OpenAlex
- W2056903366
- Cited by
- 121
- References
- 37
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 8 · 2023: 12 · 2022: 3 · 2021: 9 · 2020: 4 · 2019: 13 · 2018: 15 · 2017: 5 · 2016: 6 · 2015: 12 · 2014: 12 · 2013: 9 · 2012: 2
- FWCI
- 4.50
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- Locations
- American Journal of Kidney Diseases (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Site cant be reached (submittedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- American Society of Nephrology
- Wellcome Trust
- National Institute for Health and Care Research
- Biotechnology and Biological Sciences Research Council (G20234)
- Helmholtz Zentrum München
- 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
- serum metabolites, glomerular filtration rate, renal function, chronic kidney disease, metabolite ratios, acylcarnitines, glutarylcarnitine, metabolic phenotypes, metabolic score, serum metabolomics, creatinine, cystatin C, CKD-EPI equation, general population
- MeSH terms
- Carnitine, Creatinine, Cross-Sectional Studies, Female, Glomerular Filtration Rate, Humans, Kidney Function Tests, Male, Middle Aged, Reference Values, Renal Insufficiency, Chronic, Cystatin C, Metabolomics
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