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Urinary Metabolite Profile Predicting the Progression of CKD

KEY POINTS: As a biomarker, urinary metabolites could bridge the gap between genetic abnormalities and phenotypes of diseases. We found that levels of betaine, choline, fumarate, citrate, and glucose were significantly correlated with kidney function and could predict kidney outcomes, providing prog...

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Autores principales: Kim, Yaerim, Lee, Jueun, Kang, Mi Sun, Song, Jeongin, Kim, Seong Geun, Cho, Semin, Huh, Hyuk, Lee, Soojin, Park, Sehoon, Jo, Hyung Ah, Yang, Seung Hee, Paek, Jin Hyuk, Park, Woo Yeong, Han, Seung Seok, Lee, Hajeong, Lee, Jung Pyo, Joo, Kwon Wook, Lim, Chun Soo, Hwang, Geum-Sook, Kim, Dong Ki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Nephrology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476680/
https://www.ncbi.nlm.nih.gov/pubmed/37291728
http://dx.doi.org/10.34067/KID.0000000000000158
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author Kim, Yaerim
Lee, Jueun
Kang, Mi Sun
Song, Jeongin
Kim, Seong Geun
Cho, Semin
Huh, Hyuk
Lee, Soojin
Park, Sehoon
Jo, Hyung Ah
Yang, Seung Hee
Paek, Jin Hyuk
Park, Woo Yeong
Han, Seung Seok
Lee, Hajeong
Lee, Jung Pyo
Joo, Kwon Wook
Lim, Chun Soo
Hwang, Geum-Sook
Kim, Dong Ki
author_facet Kim, Yaerim
Lee, Jueun
Kang, Mi Sun
Song, Jeongin
Kim, Seong Geun
Cho, Semin
Huh, Hyuk
Lee, Soojin
Park, Sehoon
Jo, Hyung Ah
Yang, Seung Hee
Paek, Jin Hyuk
Park, Woo Yeong
Han, Seung Seok
Lee, Hajeong
Lee, Jung Pyo
Joo, Kwon Wook
Lim, Chun Soo
Hwang, Geum-Sook
Kim, Dong Ki
author_sort Kim, Yaerim
collection PubMed
description KEY POINTS: As a biomarker, urinary metabolites could bridge the gap between genetic abnormalities and phenotypes of diseases. We found that levels of betaine, choline, fumarate, citrate, and glucose were significantly correlated with kidney function and could predict kidney outcomes, providing prognostic biomarkers in CKD. BACKGROUND: Because CKD is caused by genetic and environmental factors, biomarker development through metabolomic analysis, which reflects gene-derived downstream effects and host adaptation to the environment, is warranted. METHODS: We measured the metabolites in urine samples collected from 789 patients at the time of kidney biopsy and from urine samples from 147 healthy participants using nuclear magnetic resonance. The composite outcome was defined as a 30% decline in eGFR, doubling of serum creatinine levels, or end-stage kidney disease. RESULTS: Among the 28 candidate metabolites, we identified seven metabolites showing (1) good discrimination between healthy controls and patients with stage 1 CKD and (2) a consistent change in pattern from controls to patients with advanced-stage CKD. Among the seven metabolites, betaine, choline, glucose, fumarate, and citrate showed significant associations with the composite outcome after adjustment for age, sex, eGFR, the urine protein–creatinine ratio, and diabetes. Furthermore, adding choline, glucose, or fumarate to traditional biomarkers, including eGFR and proteinuria, significantly improved the ability of the net reclassification improvement (P < 0.05) and integrated discrimination improvement (P < 0.05) to predict the composite outcome. CONCLUSION: Urinary metabolites, including betaine, choline, fumarate, citrate, and glucose, were found to be significant predictors of the progression of CKD. As a signature of kidney injury–related metabolites, it would be warranted to monitor to predict the renal outcome.
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spelling pubmed-104766802023-09-05 Urinary Metabolite Profile Predicting the Progression of CKD Kim, Yaerim Lee, Jueun Kang, Mi Sun Song, Jeongin Kim, Seong Geun Cho, Semin Huh, Hyuk Lee, Soojin Park, Sehoon Jo, Hyung Ah Yang, Seung Hee Paek, Jin Hyuk Park, Woo Yeong Han, Seung Seok Lee, Hajeong Lee, Jung Pyo Joo, Kwon Wook Lim, Chun Soo Hwang, Geum-Sook Kim, Dong Ki Kidney360 Original Investigation KEY POINTS: As a biomarker, urinary metabolites could bridge the gap between genetic abnormalities and phenotypes of diseases. We found that levels of betaine, choline, fumarate, citrate, and glucose were significantly correlated with kidney function and could predict kidney outcomes, providing prognostic biomarkers in CKD. BACKGROUND: Because CKD is caused by genetic and environmental factors, biomarker development through metabolomic analysis, which reflects gene-derived downstream effects and host adaptation to the environment, is warranted. METHODS: We measured the metabolites in urine samples collected from 789 patients at the time of kidney biopsy and from urine samples from 147 healthy participants using nuclear magnetic resonance. The composite outcome was defined as a 30% decline in eGFR, doubling of serum creatinine levels, or end-stage kidney disease. RESULTS: Among the 28 candidate metabolites, we identified seven metabolites showing (1) good discrimination between healthy controls and patients with stage 1 CKD and (2) a consistent change in pattern from controls to patients with advanced-stage CKD. Among the seven metabolites, betaine, choline, glucose, fumarate, and citrate showed significant associations with the composite outcome after adjustment for age, sex, eGFR, the urine protein–creatinine ratio, and diabetes. Furthermore, adding choline, glucose, or fumarate to traditional biomarkers, including eGFR and proteinuria, significantly improved the ability of the net reclassification improvement (P < 0.05) and integrated discrimination improvement (P < 0.05) to predict the composite outcome. CONCLUSION: Urinary metabolites, including betaine, choline, fumarate, citrate, and glucose, were found to be significant predictors of the progression of CKD. As a signature of kidney injury–related metabolites, it would be warranted to monitor to predict the renal outcome. American Society of Nephrology 2023-06-09 /pmc/articles/PMC10476680/ /pubmed/37291728 http://dx.doi.org/10.34067/KID.0000000000000158 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Investigation
Kim, Yaerim
Lee, Jueun
Kang, Mi Sun
Song, Jeongin
Kim, Seong Geun
Cho, Semin
Huh, Hyuk
Lee, Soojin
Park, Sehoon
Jo, Hyung Ah
Yang, Seung Hee
Paek, Jin Hyuk
Park, Woo Yeong
Han, Seung Seok
Lee, Hajeong
Lee, Jung Pyo
Joo, Kwon Wook
Lim, Chun Soo
Hwang, Geum-Sook
Kim, Dong Ki
Urinary Metabolite Profile Predicting the Progression of CKD
title Urinary Metabolite Profile Predicting the Progression of CKD
title_full Urinary Metabolite Profile Predicting the Progression of CKD
title_fullStr Urinary Metabolite Profile Predicting the Progression of CKD
title_full_unstemmed Urinary Metabolite Profile Predicting the Progression of CKD
title_short Urinary Metabolite Profile Predicting the Progression of CKD
title_sort urinary metabolite profile predicting the progression of ckd
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476680/
https://www.ncbi.nlm.nih.gov/pubmed/37291728
http://dx.doi.org/10.34067/KID.0000000000000158
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