Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease

Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strat...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hasegawa, Sho [verfasserIn] Inagi, Reiko [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Diabetic kidney disease Metabolomics Mitochondria Organelle crosstalk Uremic toxin Biomarker

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: Current diabetes reports - Heidelberg [u.a.] : Springer, 2001, 21(2021), 7 vom: 11. Mai
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:7 ; day:11 ; month:05

Links:	Volltext

DOI / URN:	10.1007/s11892-021-01390-8

Katalog-ID:	SPR044009178

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520			\|a Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD.
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author_variant	s h sh r i ri
matchkey_str	article:15390829:2021----::ansigeaooisoeciehptohsooyneligrges
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allfields	10.1007/s11892-021-01390-8 doi (DE-627)SPR044009178 (DE-599)SPRs11892-021-01390-8-e (SPR)s11892-021-01390-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.89 bkl Hasegawa, Sho verfasserin aut Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD. Diabetic kidney disease (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Mitochondria (dpeaa)DE-He213 Organelle crosstalk (dpeaa)DE-He213 Uremic toxin (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Inagi, Reiko verfasserin aut Enthalten in Current diabetes reports Heidelberg [u.a.] : Springer, 2001 21(2021), 7 vom: 11. Mai (DE-627)357168216 (DE-600)2094158-4 1539-0829 nnns volume:21 year:2021 number:7 day:11 month:05 https://dx.doi.org/10.1007/s11892-021-01390-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.89 ASE AR 21 2021 7 11 05
spelling	10.1007/s11892-021-01390-8 doi (DE-627)SPR044009178 (DE-599)SPRs11892-021-01390-8-e (SPR)s11892-021-01390-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.89 bkl Hasegawa, Sho verfasserin aut Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD. Diabetic kidney disease (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Mitochondria (dpeaa)DE-He213 Organelle crosstalk (dpeaa)DE-He213 Uremic toxin (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Inagi, Reiko verfasserin aut Enthalten in Current diabetes reports Heidelberg [u.a.] : Springer, 2001 21(2021), 7 vom: 11. Mai (DE-627)357168216 (DE-600)2094158-4 1539-0829 nnns volume:21 year:2021 number:7 day:11 month:05 https://dx.doi.org/10.1007/s11892-021-01390-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.89 ASE AR 21 2021 7 11 05
allfields_unstemmed	10.1007/s11892-021-01390-8 doi (DE-627)SPR044009178 (DE-599)SPRs11892-021-01390-8-e (SPR)s11892-021-01390-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.89 bkl Hasegawa, Sho verfasserin aut Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD. Diabetic kidney disease (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Mitochondria (dpeaa)DE-He213 Organelle crosstalk (dpeaa)DE-He213 Uremic toxin (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Inagi, Reiko verfasserin aut Enthalten in Current diabetes reports Heidelberg [u.a.] : Springer, 2001 21(2021), 7 vom: 11. Mai (DE-627)357168216 (DE-600)2094158-4 1539-0829 nnns volume:21 year:2021 number:7 day:11 month:05 https://dx.doi.org/10.1007/s11892-021-01390-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.89 ASE AR 21 2021 7 11 05
allfieldsGer	10.1007/s11892-021-01390-8 doi (DE-627)SPR044009178 (DE-599)SPRs11892-021-01390-8-e (SPR)s11892-021-01390-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.89 bkl Hasegawa, Sho verfasserin aut Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD. Diabetic kidney disease (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Mitochondria (dpeaa)DE-He213 Organelle crosstalk (dpeaa)DE-He213 Uremic toxin (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Inagi, Reiko verfasserin aut Enthalten in Current diabetes reports Heidelberg [u.a.] : Springer, 2001 21(2021), 7 vom: 11. Mai (DE-627)357168216 (DE-600)2094158-4 1539-0829 nnns volume:21 year:2021 number:7 day:11 month:05 https://dx.doi.org/10.1007/s11892-021-01390-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.89 ASE AR 21 2021 7 11 05
allfieldsSound	10.1007/s11892-021-01390-8 doi (DE-627)SPR044009178 (DE-599)SPRs11892-021-01390-8-e (SPR)s11892-021-01390-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.89 bkl Hasegawa, Sho verfasserin aut Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD. Diabetic kidney disease (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Mitochondria (dpeaa)DE-He213 Organelle crosstalk (dpeaa)DE-He213 Uremic toxin (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213 Inagi, Reiko verfasserin aut Enthalten in Current diabetes reports Heidelberg [u.a.] : Springer, 2001 21(2021), 7 vom: 11. Mai (DE-627)357168216 (DE-600)2094158-4 1539-0829 nnns volume:21 year:2021 number:7 day:11 month:05 https://dx.doi.org/10.1007/s11892-021-01390-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.89 ASE AR 21 2021 7 11 05
language	English
source	Enthalten in Current diabetes reports 21(2021), 7 vom: 11. Mai volume:21 year:2021 number:7 day:11 month:05
sourceStr	Enthalten in Current diabetes reports 21(2021), 7 vom: 11. Mai volume:21 year:2021 number:7 day:11 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Diabetic kidney disease Metabolomics Mitochondria Organelle crosstalk Uremic toxin Biomarker
dewey-raw	610
isfreeaccess_bool	true
container_title	Current diabetes reports
authorswithroles_txt_mv	Hasegawa, Sho @@aut@@ Inagi, Reiko @@aut@@
publishDateDaySort_date	2021-05-11T00:00:00Z
hierarchy_top_id	357168216
dewey-sort	3610
id	SPR044009178
language_de	englisch
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Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. 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author	Hasegawa, Sho
spellingShingle	Hasegawa, Sho ddc 610 bkl 44.89 misc Diabetic kidney disease misc Metabolomics misc Mitochondria misc Organelle crosstalk misc Uremic toxin misc Biomarker Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease
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topic_title	610 ASE 44.89 bkl Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease Diabetic kidney disease (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Mitochondria (dpeaa)DE-He213 Organelle crosstalk (dpeaa)DE-He213 Uremic toxin (dpeaa)DE-He213 Biomarker (dpeaa)DE-He213
topic	ddc 610 bkl 44.89 misc Diabetic kidney disease misc Metabolomics misc Mitochondria misc Organelle crosstalk misc Uremic toxin misc Biomarker
topic_unstemmed	ddc 610 bkl 44.89 misc Diabetic kidney disease misc Metabolomics misc Mitochondria misc Organelle crosstalk misc Uremic toxin misc Biomarker
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title	Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease
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title_full	Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease
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title_sort	harnessing metabolomics to describe the pathophysiology underlying progression in diabetic kidney disease
title_auth	Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease
abstract	Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD. © The Author(s) 2021
abstractGer	Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD. © The Author(s) 2021
abstract_unstemmed	Purpose of Review Diabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. Recent Findings Alterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Summary Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD. © The Author(s) 2021
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container_issue	7
title_short	Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease
url	https://dx.doi.org/10.1007/s11892-021-01390-8
remote_bool	true
author2	Inagi, Reiko
author2Str	Inagi, Reiko
ppnlink	357168216
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1007/s11892-021-01390-8
up_date	2024-07-03T22:20:22.095Z
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