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. Issue 7 (25th March 2020)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Diabetes, obesity & metabolism. Volume 22:Issue 7(2020)
• Page Start: 1157
• Page End: 1166
• Publication Date: 2020-03-25
• Subjects: bioinformatics -- dapagliflozin -- kidney function -- metabolomics -- sodium‐glucose co‐transporter‐2 -- type 2 diabetes; Diabetes -- Periodicals; Obesity -- Periodicals; Metabolism -- Disorders -- Periodicals; Clinical pharmacology -- Periodicals; 616.462
• DOI: 10.1111/dom.14018
• Languages: English
• ISSNs: 1462-8902

Abstract

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.