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A metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes

AIM: To investigate which metabolic pathways are targeted by the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin to explore the molecular processes involved in its renal protective effects. METHODS: An unbiased mass spectrometry plasma metabolomics assay was performed on baseline and follow‐...

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Autores principales: Mulder, Skander, Hammarstedt, Ann, Nagaraj, Sunil B., Nair, Viji, Ju, Wenjun, Hedberg, Jonatan, Greasley, Peter J., Eriksson, Jan W., Oscarsson, Jan, Heerspink, Hiddo J. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317707/
https://www.ncbi.nlm.nih.gov/pubmed/32115853
http://dx.doi.org/10.1111/dom.14018
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author Mulder, Skander
Hammarstedt, Ann
Nagaraj, Sunil B.
Nair, Viji
Ju, Wenjun
Hedberg, Jonatan
Greasley, Peter J.
Eriksson, Jan W.
Oscarsson, Jan
Heerspink, Hiddo J. L.
author_facet Mulder, Skander
Hammarstedt, Ann
Nagaraj, Sunil B.
Nair, Viji
Ju, Wenjun
Hedberg, Jonatan
Greasley, Peter J.
Eriksson, Jan W.
Oscarsson, Jan
Heerspink, Hiddo J. L.
author_sort Mulder, Skander
collection PubMed
description AIM: To investigate which metabolic pathways are targeted by the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin to explore the molecular processes involved in its renal protective effects. METHODS: An unbiased mass spectrometry plasma metabolomics assay was performed on baseline and follow‐up (week 12) samples from the EFFECT II trial in patients with type 2 diabetes with non‐alcoholic fatty liver disease receiving dapagliflozin 10 mg/day (n = 19) or placebo (n = 6). Transcriptomic signatures from tubular compartments were identified from kidney biopsies collected from patients with diabetic kidney disease (DKD) (n = 17) and healthy controls (n = 30) from the European Renal cDNA Biobank. Serum metabolites that significantly changed after 12 weeks of dapagliflozin were mapped to a metabolite‐protein interaction network. These proteins were then linked with intra‐renal transcripts that were associated with DKD or estimated glomerular filtration rate (eGFR). The impacted metabolites and their protein‐coding transcripts were analysed for enriched pathways. RESULTS: Of all measured (n = 812) metabolites, 108 changed (P < 0.05) during dapagliflozin treatment and 74 could be linked to 367 unique proteins/genes. Intra‐renal mRNA expression analysis of the genes encoding the metabolite‐associated proteins using kidney biopsies resulted in 105 genes that were significantly associated with eGFR in patients with DKD, and 135 genes that were differentially expressed between patients with DKD and controls. The combination of metabolites and transcripts identified four enriched pathways that were affected by dapagliflozin and associated with eGFR: glycine degradation (mitochondrial function), TCA cycle II (energy metabolism), L‐carnitine biosynthesis (energy metabolism) and superpathway of citrulline metabolism (nitric oxide synthase and endothelial function). CONCLUSION: The observed molecular pathways targeted by dapagliflozin and associated with DKD suggest that modifying molecular processes related to energy metabolism, mitochondrial function and endothelial function may contribute to its renal protective effect.
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spelling pubmed-73177072020-06-29 A metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes Mulder, Skander Hammarstedt, Ann Nagaraj, Sunil B. Nair, Viji Ju, Wenjun Hedberg, Jonatan Greasley, Peter J. Eriksson, Jan W. Oscarsson, Jan Heerspink, Hiddo J. L. Diabetes Obes Metab Original Articles AIM: To investigate which metabolic pathways are targeted by the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin to explore the molecular processes involved in its renal protective effects. METHODS: An unbiased mass spectrometry plasma metabolomics assay was performed on baseline and follow‐up (week 12) samples from the EFFECT II trial in patients with type 2 diabetes with non‐alcoholic fatty liver disease receiving dapagliflozin 10 mg/day (n = 19) or placebo (n = 6). Transcriptomic signatures from tubular compartments were identified from kidney biopsies collected from patients with diabetic kidney disease (DKD) (n = 17) and healthy controls (n = 30) from the European Renal cDNA Biobank. Serum metabolites that significantly changed after 12 weeks of dapagliflozin were mapped to a metabolite‐protein interaction network. These proteins were then linked with intra‐renal transcripts that were associated with DKD or estimated glomerular filtration rate (eGFR). The impacted metabolites and their protein‐coding transcripts were analysed for enriched pathways. RESULTS: Of all measured (n = 812) metabolites, 108 changed (P < 0.05) during dapagliflozin treatment and 74 could be linked to 367 unique proteins/genes. Intra‐renal mRNA expression analysis of the genes encoding the metabolite‐associated proteins using kidney biopsies resulted in 105 genes that were significantly associated with eGFR in patients with DKD, and 135 genes that were differentially expressed between patients with DKD and controls. The combination of metabolites and transcripts identified four enriched pathways that were affected by dapagliflozin and associated with eGFR: glycine degradation (mitochondrial function), TCA cycle II (energy metabolism), L‐carnitine biosynthesis (energy metabolism) and superpathway of citrulline metabolism (nitric oxide synthase and endothelial function). CONCLUSION: The observed molecular pathways targeted by dapagliflozin and associated with DKD suggest that modifying molecular processes related to energy metabolism, mitochondrial function and endothelial function may contribute to its renal protective effect. Blackwell Publishing Ltd 2020-03-25 2020-07 /pmc/articles/PMC7317707/ /pubmed/32115853 http://dx.doi.org/10.1111/dom.14018 Text en © 2020 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Articles
Mulder, Skander
Hammarstedt, Ann
Nagaraj, Sunil B.
Nair, Viji
Ju, Wenjun
Hedberg, Jonatan
Greasley, Peter J.
Eriksson, Jan W.
Oscarsson, Jan
Heerspink, Hiddo J. L.
A metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes
title A metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes
title_full A metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes
title_fullStr A metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes
title_full_unstemmed A metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes
title_short A metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes
title_sort metabolomics‐based molecular pathway analysis of how the sodium‐glucose co‐transporter‐2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317707/
https://www.ncbi.nlm.nih.gov/pubmed/32115853
http://dx.doi.org/10.1111/dom.14018
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