Publication

A metabolomics based molecular pathway analysis for how the SGLT2-inhibitor dapagliflozin may slow kidney function decline in patients with diabetes

Mulder, S., Hammarstedt, A., Nagaraj, S. B., Nair, V., Ju, W., Hedberg, J., Greasley, P. J., Eriksson, J. W., Oscarsson, J. & Heerspink, H. J. L., Jul-2020, In : Diabetes obesity & metabolism. 22, 7, p. 1157-1166 10 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Mulder, S., Hammarstedt, A., Nagaraj, S. B., Nair, V., Ju, W., Hedberg, J., Greasley, P. J., Eriksson, J. W., Oscarsson, J., & Heerspink, H. J. L. (2020). A metabolomics based molecular pathway analysis for how the SGLT2-inhibitor dapagliflozin may slow kidney function decline in patients with diabetes. Diabetes obesity & metabolism, 22(7), 1157-1166. https://doi.org/10.1111/dom.14018

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. / A metabolomics based molecular pathway analysis for how the SGLT2-inhibitor dapagliflozin may slow kidney function decline in patients with diabetes. In: Diabetes obesity & metabolism. 2020 ; Vol. 22, No. 7. pp. 1157-1166.

Harvard

Mulder, S, Hammarstedt, A, Nagaraj, SB, Nair, V, Ju, W, Hedberg, J, Greasley, PJ, Eriksson, JW, Oscarsson, J & Heerspink, HJL 2020, 'A metabolomics based molecular pathway analysis for how the SGLT2-inhibitor dapagliflozin may slow kidney function decline in patients with diabetes', Diabetes obesity & metabolism, vol. 22, no. 7, pp. 1157-1166. https://doi.org/10.1111/dom.14018

Standard

A metabolomics based molecular pathway analysis for how the SGLT2-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.

In: Diabetes obesity & metabolism, Vol. 22, No. 7, 07.2020, p. 1157-1166.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Mulder S, Hammarstedt A, Nagaraj SB, Nair V, Ju W, Hedberg J et al. A metabolomics based molecular pathway analysis for how the SGLT2-inhibitor dapagliflozin may slow kidney function decline in patients with diabetes. Diabetes obesity & metabolism. 2020 Jul;22(7):1157-1166. https://doi.org/10.1111/dom.14018


BibTeX

@article{28ef9dcfabfe43a0ae30adc423d305b7,
title = "A metabolomics based molecular pathway analysis for how the SGLT2-inhibitor dapagliflozin may slow kidney function decline in patients with diabetes",
abstract = "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.",
keywords = "bioinformatics, dapagliflozin, kidney function, metabolomics, sodium-glucose co-transporter-2, type 2 diabetes, NEPHROPATHY, CONTRIBUTE, RATIONALE, PROTOCOL",
author = "Skander Mulder and Ann Hammarstedt and Nagaraj, {Sunil B} and Viji Nair and Wenjun Ju and Jonatan Hedberg and Greasley, {Peter J} and Eriksson, {Jan W} and Jan Oscarsson and Heerspink, {Hiddo J L}",
note = "This article is protected by copyright. All rights reserved.",
year = "2020",
month = jul,
doi = "10.1111/dom.14018",
language = "English",
volume = "22",
pages = "1157--1166",
journal = "Diabetes obesity & metabolism",
issn = "1462-8902",
publisher = "Wiley",
number = "7",

}

RIS

TY - JOUR

T1 - A metabolomics based molecular pathway analysis for how the SGLT2-inhibitor dapagliflozin may slow kidney function decline in patients with diabetes

AU - Mulder, Skander

AU - Hammarstedt, Ann

AU - Nagaraj, Sunil B

AU - Nair, Viji

AU - Ju, Wenjun

AU - Hedberg, Jonatan

AU - Greasley, Peter J

AU - Eriksson, Jan W

AU - Oscarsson, Jan

AU - Heerspink, Hiddo J L

N1 - This article is protected by copyright. All rights reserved.

PY - 2020/7

Y1 - 2020/7

N2 - 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.

AB - 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.

KW - bioinformatics

KW - dapagliflozin

KW - kidney function

KW - metabolomics

KW - sodium-glucose co-transporter-2

KW - type 2 diabetes

KW - NEPHROPATHY

KW - CONTRIBUTE

KW - RATIONALE

KW - PROTOCOL

U2 - 10.1111/dom.14018

DO - 10.1111/dom.14018

M3 - Article

C2 - 32115853

VL - 22

SP - 1157

EP - 1166

JO - Diabetes obesity & metabolism

JF - Diabetes obesity & metabolism

SN - 1462-8902

IS - 7

ER -

ID: 119378287