Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease

The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice

Kidney Age Index (KAI):A novel age-related biomarker to estimate kidney function in patients with diabetic kidney disease using machine learning

BACKGROUND AND OBJECTIVE: With aging, patients with diabetic kidney disease (DKD) show progressive decrease in kidney function. We investigated whether the deviation of biological age (BA) from the chronological age (CA) due to DKD can be used (denoted as Kidney Age Index; KAI) to quantify kidney function using machine learning algorithms. METHODS: Three large datasets were used in this study to develop KAI. The machine learning algorithms were trained on PREVEND dataset with healthy subjects (N = 7963) using 13 clinical markers to predict the CA. The trained model was then used to predict the BA of patients with DKD using RENAAL (N = 1451) and IDNT (N = 1706). The performance of four traditional machine learning algorithms were evaluated and the KAI = BA-CA was estimated for each patient. RESULTS: The neural network model achieved the best performance and predicted the CA of healthy subjects in PREVEND dataset with a mean absolute deviation (MAD) = 6.5 ± 3.5 years and pearson correlation = 0.62. Patients with DKD showed a significant higher KAI of 15.4 ± 11.8 years and 13.6 ± 12.3 years in RENAAL and IDNT datasets, respectively. CONCLUSIONS: Our findings suggest that for a given CA, patients with DKD shows excess BA when compared to their healthy counterparts due to disease severity. With further improvement, the proposed KAI can be used as a complementary easy-to-interpret tool to give a more inclusive idea into disease state

Expression of Nek1 during kidney development and cyst formation in multiple nephron segments in the Nek1-deficient kat2J mouse model of polycystic kidney disease

BACKGROUND: Neks, mammalian orthologs of the fungal protein kinase never-in-mitosis A, have been implicated in the pathogenesis of polycystic kidney disease. Among them, Nek1 is the primary protein inactivated in kat2J mouse models of PKD. RESULT: We report the expression pattern of Nek1 and characterize the renal cysts that develop in kat2J mice. Nek1 is detectable in all murine tissues but its expression in wild type and kat2J heterozygous kidneys decrease as the kidneys mature, especially in tubular epithelial cells. In the embryonic kidney, Nek1 expression is most prominent in cells that will become podocytes and proximal tubules. Kidney development in kat2J homozygous mice is aberrant early, before the appearance of gross cysts: developing cortical zones are thin, populated by immature glomeruli, and characterized by excessive apoptosis of several cell types. Cysts in kat2J homozygous mice form postnatally in Bowman’s space as well as different tubular subtypes. Late in life, kat2J heterozygous mice form renal cysts and the cells lining these cysts lack staining for Nek1. The primary cilia of cells lining cysts in kat2J homozygous mice are morphologically diverse: in some cells they are unusually long and in others there are multiple cilia of varying lengths. CONCLUSION: Our studies indicate that Nek1 deficiency leads to disordered kidney maturation, and cysts throughout the nephron

Thiazide diuretics and the rate of disease progression in autosomal dominant polycystic kidney disease:an observational study

BACKGROUND: In autosomal dominant polycystic kidney disease (ADPKD), hypertension is prevalent and cardiovascular events are the main cause of death. Thiazide diuretics are often prescribed as second-line antihypertensives, on top of renin-angiotensin-aldosterone system (RAAS) blockade. There is a concern, however, that diuretics may increase vasopressin concentration and RAAS activity, thereby worsening disease progression in ADPKD. We aimed to investigate the validity of these suggestions. METHODS: We analysed an observational cohort of 533 ADPKD patients. Plasma copeptin (surrogate for vasopressin), aldosterone and renin were measured by enzyme-linked immunosorbent assay and radioimmunoassay, respectively. Linear mixed models were used to assess the association of thiazide use with estimated glomerular filtration rate (eGFR) decline and Cox proportional hazards models for the association with the composite kidney endpoint of incident end-stage kidney disease, 40% eGFR decline or death. RESULTS: A total of 23% of participants (n = 125) used thiazide diuretics at baseline. Compared with non-users, thiazide users were older, a larger proportion was male, they had lower eGFRs and similar blood pressure under more antihypertensives. Plasma copeptin was higher, but this difference disappeared after adjustment for age and sex. Both renin and aldosterone were higher in thiazide users. There was no difference between thiazide users and non-users in the rate of eGFR decline {difference -0.35 mL/min/1.73 m2 per year [95% confidence interval (CI) -0.83 to -0.14], P = 0.2} during 3.9 years of follow-up (interquartile range 2.5-4.9). This did not change after adjustment for potential confounders [difference final model: 0.08 mL/min/1.73 m2 per year [95% CI -0.46 to -0.62], P = 0.8). In the crude model, thiazide use was associated with a higher incidence of the composite kidney endpoint [hazard ratio (HR) 1.53 (95% CI 1.05-2.23), P = 0.03]. However, this association lost significance after adjustment for age and sex and remained unassociated after adjustment for additional confounders [final model: HR 0.80 (95% CI 0.50-1.29), P = 0.4]. CONCLUSIONS: These data do not show that thiazide diuretics have a detrimental effect on the rate of disease progression in ADPKD and suggest that these drugs can be prescribed as second-line antihypertensives

The effects of dapagliflozin on urinary metabolites in patients with type 2 diabetes

AIMS: Previously, a panel of 13 urinary metabolites linked to mitochondrial metabolism was found to be significantly reduced in patients with diabetic kidney disease and eGFR>60 ml/min/1.73m2 . The beneficial effects of SGLT-2 inhibition on cardio-renal outcomes are hypothesized in part due to improved work efficiency at the mitochondrial level. We therefore assessed the effects of the SGLT-2 inhibitor dapagliflozin, on this pre-specified panel of 13 urinary metabolites linked to mitochondrial metabolism in patients with type 2 diabetes and elevated albuminuria. MATERIALS AND METHODS: Urine and plasma samples were used from a double-blind, randomized, placebo controlled crossover trial in 31 patients with type 2 diabetes, albumin:creatinine ratio >100 mg/g, and on a stable dose of an Angiotensin Converting Enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB). Dapagliflozin or placebo treatment periods each lasted for 6 weeks, with 6 weeks wash-out in between. Urinary and plasma metabolites were quantified by gas-chromatography mass spectrometry, corrected for creatinine, 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%CI: 8.5 - 85.6, p=0.01) with placebo compared to 121% (69 - 189, p<0.001) with dapaglifozin. Accordingly, the placebo-adjusted effect was 56% (11 - 118, p=0.012). In plasma, seven of the 13 metabolites were detectable, and none were modified by dapagliflozin. CONCLUSIONS: Dapagliflozin significantly increased a panel of urinary metabolites previously linked to mitochondrial metabolism. These data support the hypothesis that SGLT-2 inhibitors may improve mitochondrial function, and improvements in mitochondrial function may be a mechanism for kidney protection. Future studies of longer treatment duration and clinical outcomes are needed to confirm the clinical impact of these findings. This article is protected by copyright. All rights reserved

Prediction of the effect of dapagliflozin on kidney and heart failure outcomes based on short-term changes in multiple risk markers

BACKGROUND: Besides improving glucose control, sodium-glucose co-transporter 2 inhibition with dapagliflozin reduces blood pressure, body weight and urinary albumin:creatinine ratio (UACR) in patients with type 2 diabetes (T2DM). The parameter response efficacy (PRE) score was developed to predict how short-term drug effects on cardiovascular risk markers translate into long-term changes in clinical outcomes. We applied the PRE score to clinical trials of dapagliflozin to model the effect of the drug on kidney and heart failure (HF) outcomes in patients with T2DM and impaired kidney function. METHODS: The relationships between multiple risk markers and long-term outcome were determined in a background population of patients with T2DM with a multivariable Cox model. These relationships were then applied to short-term changes in risk markers observed in a pooled database of dapagliflozin trials (n = 7) that recruited patients with albuminuria to predict the drug-induced changes to kidney and HF outcomes. RESULTS: A total of 132 and 350 patients had UACR >200 mg/g and >30 mg/g at baseline, respectively, and were selected for analysis. The PRE score predicted a risk change for kidney events of -40.8% [95% confidence interval (CI) -51.7 to -29.4) and -40.4% (95% CI -48.4 to -31.1) with dapagliflozin 10 mg compared with placebo for the UACR >200 mg/g and >30 mg/g subgroups. The predicted change in risk for HF events was -27.3% (95% CI -47.7 to -5.1) and -21.2% (95% CI -35.0 to -7.8), respectively. Simulation analyses showed that even with a smaller albuminuria-lowering effect of dapagliflozin (10% instead of the observed 35% in both groups), the estimated kidney risk reduction was still 26.5 and 26.8%, respectively. CONCLUSIONS: The PRE score predicted clinically meaningful reductions in kidney and HF events associated with dapagliflozin therapy in patients with diabetic kidney disease. These results support a large long-term outcome trial in this population to confirm the benefits of the drug on these endpoints

Precision Nephrology Is a Non-Negligible State of Mind in Clinical Research:Remember the Past to Face the Future

CKD is a major public health problem. It is characterized by a multitude of risk factors that, when aggregated, can strongly modify outcome. While major risk factors, namely, albuminuria and low estimated glomerular filtration rate (eGFR) have been well analyzed, a large variability in disease progression still remains. This happens because (1) the weight of each risk factor varies between populations (general population or CKD cohort), countries, and single individuals and (2) response to nephroprotective drugs is so heterogeneous that a non-negligible part of patients maintains a high cardiorenal risk despite optimal treatment. Precision nephrology aims at individualizing cardiorenal prognosis and therapy. The purpose of this review is to focus on the risk stratification in different areas, such as clinical practice, population research, and interventional trials, and to describe the strategies used in observational or experimental studies to afford individual-level evidence. The future of precision nephrology is also addressed. Observational studies can in fact provide more adequate findings by collecting more information on risk factors and building risk prediction models that can be applied to each individual in a reliable fashion. Similarly, new clinical trial designs can reduce the individual variability in response to treatment and improve individual outcomes

Baseline urinary metabolites predict albuminuria response to spironolactone in type 2 diabetes

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 -42% (25th to 75% percentile -65 to 6) and -29% (25th to 75% percentile -37 to -1) 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 -58% (25th to 75% percentile -78 to 33), -28% (25th to 75% percentile -46 to 8), -40% (25th to 75% percentile -52% to 31), respectively, P= 0.001 for trend). In the replication cohort, UACR reduction was -54% (25th to 75% percentile -65 to -50), -41 (25th to 75% percentile -46% to 30), and -17% (25th to 75% percentile -36 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