Aims/hypothesis The sodium–glucose cotransporter 2 (SGLT2) inhibitor canagliflozin slows progression of kidney function decline in type 2 diabetes. The aim of this study was to assess the effect of the SGLT2 inhibitor canagliflozin on biomarkers for progression of diabetic kidney disease (DKD). Methods A canagliflozin mechanism of action (MoA) network model was constructed based on an in vitro transcriptomics experiment in human proximal tubular cells and molecular features linked to SGLT2 inhibitors from scientific literature. This model was mapped onto an established DKD network model that describes molecular processes associated with DKD. Overlapping areas in both networks were subsequently used to select candidate biomarkers that change with canagliflozin therapy. These biomarkers were measured in 296 stored plasma samples from a previously reported 2 year clinical trial comparing canagliflozin with glimepiride. Results Forty-four proteins present in the canagliflozin MoA molecular model overlapped with proteins in the DKD network model. These proteins were considered candidates for monitoring impact of canagliflozin on DKD pathophysiology. For ten of these proteins, scientific evidence was available suggesting that they are involved in DKD progression. Of these, compared with glimepiride, canagliflozin 300 mg/day decreased plasma levels of TNF receptor 1 (TNFR1; 9.2%; p < 0.001), IL-6 (26.6%; p = 0.010), matrix metalloproteinase 7 (MMP7; 24.9%; p = 0.011) and fibronectin 1 (FN1; 14.9%; p = 0.055) during 2 years of follow-up. Conclusions/interpretation The observed reduction in TNFR1, IL-6, MMP7 and FN1 suggests that canagliflozin contributes to reversing molecular processes related to inflammation, extracellular matrix turnover and fibrosis. Trial registration ClinicalTrials.gov NCT00968812 Electronic supplementary material The online version of this article (10.1007/s00125-019-4859-4) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

RAS intervention early in the course of proteinuric DKD is more beneficial than late intervention in delaying ESRD.

AimTo 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.MethodsAn 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.ResultsOf 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).ConclusionThe 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.

Aim To assess the effects of the sodium‐glucose co‐transporter‐2 (SGLT2) inhibitor dapagliflozin on a pre‐specified panel of 13 urinary metabolites linked to mitochondrial metabolism in people with type 2 diabetes and elevated urine albumin levels. Materials and methods Urine and plasma samples were used from a double‐blind, randomized, placebo‐controlled crossover trial in 31 people with type 2 diabetes, with an albumin:creatinine ratio >100 mg/g, and who were on a stable dose of an angiotensin‐converting enzyme inhibitor or an angiotensin receptor blocker. Dapagliflozin or placebo treatment periods each lasted 6 weeks, with a 6‐week washout period in between. Urinary and plasma metabolites were quantified by gas‐chromatography mass spectrometry, corrected for creatinine level, and then combined into a single‐valued urinary metabolite index. Fractional excretion of the metabolites was calculated. Results All 13 urinary metabolites were detectable. After 6 weeks of dapagliflozin therapy, nine of the 13 metabolites were significantly increased from baseline. The urinary metabolite index increased by 42% (95% confidence interval [CI] 8.5 to 85.6; P = .01) with placebo versus 121% (95% CI 69 to 189; P < .001) with dapaglifozin. The placebo‐adjusted effect was 56% (95% CI 11 to 118; P = .012). In plasma, seven of the 13 metabolites were detectable, and none was modified by dapagliflozin. Conclusions Dapagliflozin significantly increased a panel of urinary metabolites previously linked to mitochondrial metabolism. These data support the hypothesis that SGLT2 inhibitors improve mitochondrial function, and improvements in mitochondrial function could be a mechanism for kidney protection. Future studies with longer treatment duration and clinical outcomes are needed to confirm the clinical impact of these findings.

Current therapeutic approaches are ineffective in many patients with established Diabetic Kidney Disease (DKD) disease, an epidemic affecting one in three patients with diabetes. Early identification of patients at high risk for progression and individualizing therapies have the potential to mitigate kidney complications due to diabetes. To achieve this, a better understanding of the complex pathophysiology of DKD is needed. A system biology approach integrating large scale omic data is well suited to unravel the molecular mechanisms driving DKD and may offer new perspectives how to personalize therapy. Recent studies indeed demonstrate that integrating genome scale data sets generated from prospectively designed clinical cohort studies with model systems using innovative bioinformatics analysis revealed critical molecular pathways in DKD and led to the development of candidate prognostic molecular biomarkers. This review seeks to provide an overview of the recent progress in the application of the integrative systems biology approaches specifically in the field of molecular biomarkers for DKD. We will mainly focus the discussion on how to use integrative system biology approach to firstly identify patients at high risk of progression, and secondly to identify patients who may or may not respond to treatment. Challenges and opportunities in applying precision medicine in DKD will also be discussed.

The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in subjects with diabetic kidney disease, albeit with a large variability between individuals. Identifying novel biomarkers that predict response to therapy may help to tailor spironolactone therapy. We aimed to identify a set of metabolites for prediction of albuminuria response to spironolactone in subjects with type 2 diabetes. Systems biology molecular process analysis was performed a priori to identify metabolites linked to molecular disease processes and drug mechanism of action. Individual subject data and urine samples were used from 2 randomized placebo controlled double blind clinical trials (NCT01062763, NCT00381134). A urinary metabolite score was developed to predict albuminuria response to spironolactone therapy using penalized ridge regression with leave-one-out cross validation. Bioinformatic analysis identified a set of 18 metabolites linked to a diabetic kidney disease molecular model and potentially affected by spironolactone mechanism of action. Spironolactone reduced UACR relative to placebo by median À42% (25th to 75% percentile À65 to 6) and À29% (25th to 75% percentile À37 to À1) in the test and replication cohorts, respectively. In the test cohort, UACR reduction was higher in the lowest tertile of the baseline urinary metabolite score compared with middle and upper tertiles À58% (25th to 75% percentile À78 to 33), À28% (25th to 75% percentile À46 to 8), À40% (25th to 75% percentile À52% to 31), respectively, P = 0.001 for trend). In the replication cohort, UACR reduction was À54% (25th to 75% percentile À65 to À50), À41 (25th to 75% percentile À46% to 30), and À17% (25th to 75% percentile À36 to 5), respectively, P = 0.010 for trend). We identified a set of 18 urinary metabolites through systems biology to predict albuminuria response to spironolactone in type 2 diabetes. These data suggest that urinary metabolites may be used as a tool to tailor optimal therapy and move in the direction of personalized medicine.

Aim: To test whether a screening approach with more flexible urinary albumin creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) thresholds would decrease screen failure rate without negatively impacting on the event rate and overall study duration.Methods: We performed a post-hoc analysis of the ALTITUDE trial. We selected participants randomized to placebo with a UACR of >300 mg/g and an eGFR between 30 mL/min/1.73 m 2 and 60 mL/min/1.73 m 2 at the first visit (pre-screening) for the

A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care.