Abstract: FR-OR116
A Metabolomics-Based Pathway Analysis for How Dapagliflozin May Slow Kidney Function Decline in Patients with Type 2 Diabetes
Session Information
- Translating Discovery to Patients with Diabetic Kidney Disease
November 08, 2019 | Location: 207, Walter E. Washington Convention Center
Abstract Time: 05:30 PM - 05:42 PM
Category: Diabetic Kidney Disease
- 602 Diabetic Kidney Disease: Clinical
Authors
- Mulder, Skander, University Medical Center Groningen, Groningen, Netherlands
- Hammarstedt, Ann, AstraZeneca, Mölndal, Sweden
- Belur nagaraj, Sunil, University Medical Center Groningen, Groningen, Netherlands
- Greasley, Peter J., AstraZeneca, Mölndal, Sweden
- Hedberg, Jonatan, AstraZeneca, Mölndal, Sweden
- Nair, Viji, University of Michigan, Ann Arbor, Michigan, United States
- Ju, Wenjun, University of Michigan, Ann Arbor, Michigan, United States
- Eriksson, Jan W., Uppsala University, Uppsala, Sweden
- Oscarsson, Jan, AstraZeneca, Mölndal, Sweden
- L Heerspink, Hiddo Jan, University Medical Center Groningen, Groningen, Netherlands
Background
Sodium glucose cotransporter 2 inhibitors (SGLT-2i) slow progression of diabetic kidney disease (DKD). The underlying mechanisms are not fully elucidated. We examined which metabolic pathways are targeted by the SGLT-2i dapagliflozin (DAPA) to explore the molecular processes involved in the renal protective effects with DAPA.
Methods
An unbiased serum metabolomics assay was performed on baseline and follow-up (week 12) samples from the EFFECT II trial in type 2 diabetes patients with non-alcoholic fatty liver disease (NCT02279407; Eriksson Diabetologia 2018), using the DAPA 10 mg/day treatment arm (n=19). Transcriptomic signatures from tubular compartments were identified from kidney biopsies collected from patients with DKD and healthy controls from the European Renal cDNA Biobank (ERCB). Serum metabolites that were significantly changed after 12 weeks of DAPA treatment were selected and mapped to a metabolite-protein interaction network. These proteins were then linked with intra-renal transcripts that were associated with DKD or eGFR. The impacted metabolites and their protein coding transcripts were analyzed for enriched pathways.
Results
Of all measured (n=812) metabolites, 108 changed (p<0.05) during DAPA treatment and 74 could be linked to 367 unique proteins with corresponding coding 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 from the ERCB cohort, 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 DAPA and associated with eGFR: Glycine Degradation (Creatine Biosynthesis) [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 DAPA and associated with DKD suggest that modifying molecular processes related to energy metabolism, mitochondrial function, and endothelial function may contribute to the renal protective effects of DAPA.