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Metabolites associate with kidney function decline and incident chronic kidney disease in the general population
Abstract
BACKGROUND: Serum metabolites are associated cross-sectionally with kidney function in population-based studies. METHODS: Using flow injection and liquid chromatography tandem mass spectrometry methods, we examined longitudinal associations of baseline concentrations of 140 metabolites and their 19 460 ratios with kidney function decline and chronic kidney disease (CKD) incidence over 7 years in 1104 participants of the Cooperative Health Research in the Region of Augsburg S4/F4 study. RESULTS: Corrected for multiple testing, a significant association with annual change in the estimated glomerular filtration rate was observed for spermidine (P = 5.8 × 10(-7)) and two metabolite ratios, the phosphatidylcholine diacyl C42:5-to-phosphatidylcholine acyl-alkyl C36:0 ratio (P = 1.5 × 10(-6)) and the kynurenine-to-tryptophan ratio (P = 1.9 × 10(-6)). The kynurenine-to-tryptophan ratio was also associated with significantly higher incidence of CKD at the follow-up visit with an odds ratio of 1.36 per standard deviation increase; 95% confidence interval 1.11-1.66, P = 2.7 × 10(-3)). In separate analyses, the predictive ability of the metabolites was assessed: both the three significantly associated metabolite (ratios) as well as a panel of 35 metabolites selected from all metabolites in an unbiased fashion provided as much but not significantly more prognostic information than selected clinical predictors as judged by the area under the curve. CONCLUSIONS: Baseline serum concentrations of spermidine and two metabolite ratios were associated with kidney function change over subsequent years in the general population. In separate analyses, baseline serum metabolites were able to predict incident CKD to a similar but not better extent than selected clinical parameters. Our longitudinal findings provide a basis for targeted studies of certain metabolic pathways, e.g. tryptophan metabolism, and their relation to kidney function decline.
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“Such dys-regulation was associated with eGFR decline and CKD incidence in a population-based study183.”
From The gut microbiota and the brain–gut–kidney axis in hypertension and chronic kidney disease (2018) · cites this paper as [183]
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“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 [183]
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A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603
Predictors of New-Onset Kidney Disease in a Community-Based Population
Fox, C. S. (2004) · JAMA · doi:10.1001/jama.291.7.844 · Cited by 1,241 · cited 1×
“Associations of changes in eGFR with metabolites and ratios were obtained from linear regression analyses, adjusted for known CKD risk factors measured at baseline: age (years) [11], gender, body mass index (BMI, kg/m(2)), systolic blood pressure (mmHg), current smoking status, antihypertensive medication use, antihyp…”
MethodPrevalence of chronic kidney disease in population-based studies: Systematic review
Zhang, Q.-L., & Rothenbacher, D. (2008) · BMC Public Health · doi:10.1186/1471-2458-8-117 · Cited by 1,045
Coresh, J., Astor, B. C., McQuillan, G. M., Kusek, J. W., Greene, T., Lente, F. V., & Levey, A. S. (2002) · American Journal of Kidney Diseases · doi:10.1053/ajkd.2002.32765 · Cited by 714
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Illig, T., Gieger, C., Zhai, G., Römisch-Margl, W., Wang-Sattler, R., Prehn, C., Altmaier, E., Kastenmüller, G., Kato, B. S., Mewes, H.-W., et al. (2010) · Nature Genetics · doi:10.1038/ng.507 · Cited by 653
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Selvin, E., Manzi, J., Stevens, L. A., Lente, F. V., Lacher, D. A., Levey, A. S., & Coresh, J. (2007) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2007.08.020 · Cited by 307
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Hou, Y., Xiao, Z., Zhu, Y., Li, Y., Liu, Q., & Wang, Z. (2024) · Figshare · doi:10.6084/m9.figshare.25919052
Cirillo, A., Résimont, G., Massias, J., Guitton, Y., Jouret, F., Vidal‐Petiot, E., Flamant, M., Delanaye, P., & Tullio, P. D. (2026) · Research Square · doi:10.21203/rs.3.rs-8620894/v1
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Self-citation张, 佩. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1631013
Li, Y., Gong, X., Bai, Y., Yan, L., An, Y., Liu, H., Zhang, H., & Dai, X. (2026) · BMC Nephrology · doi:10.1186/s12882-026-05000-1
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Metadata
- Authors
- Goek, O.-N., Prehn, C., Sekula, P., Römisch-Margl, W., Döring, A., Gieger, C., Heier, M., Koenig, W., Wang-Sattler, R., Illig, T., Suhre, K., Adamski, J., Köttgen, A., & Meisinger, C.
- Affiliations
- University Medical Center Freiburg (DE)
- Helmholtz Munich (DE)
- Johns Hopkins University (US)
- Corresponding author
- Goek, Oemer-Necmi
- ORCID
- Venue
- Nephrology Dialysis Transplantation
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-05
- Volume
- 28
- Issue
- 8
- Pages
- 2131-2138
- ISSN
- 0931-0509 (ISSN-L)
- 1460-2385
- OpenAlex
- W2147888063
- Cited by
- 143
- References
- 28
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 9 · 2023: 13 · 2022: 8 · 2021: 17 · 2020: 7 · 2019: 18 · 2018: 11 · 2017: 15 · 2016: 8 · 2015: 9 · 2014: 9 · 2013: 1
- FWCI
- 4.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nephrology Dialysis Transplantation (publishedVersion)
- PubMed (publishedVersion)
- Site cant be reached (submittedVersion)
- OPUS (Augsburg University) (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
- serum metabolites, renal function decline, estimated glomerular filtration rate, spermidine, kynurenine/tryptophan ratio, tryptophan metabolism, metabolite ratios, prognostic factors, metabolic pathways
- MeSH terms
- Aged, Female, Follow-Up Studies, Germany, Glomerular Filtration Rate, Humans, Kidney Function Tests, Longitudinal Studies, Male, Middle Aged, Prognosis, Risk Factors, ROC Curve, Spectrometry, Mass, Electrospray Ionization, Renal Insufficiency, Chronic, Tandem Mass Spectrometry, Metabolomics, Metabolome
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