Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes

Rationale and design of the Innsbruck Diabetic Kidney Disease Cohort (IDKDC)-a prospective study investigating etiology and progression of early-stage chronic kidney disease in type 2 diabetes

Background

The development of chronic kidney disease (CKD) in about 20%-40% of patients with type 2 diabetes (T2D) aggravates cardiovascular morbidity and mortality. Pathophysiology is of increasing relevance for individual management and prognosis, though it is largely unknown among T2D patients with CKD as histologic work-up is not routinely performed upon typical clinical presentation. However, as clinical parameters do not appropriately reflect underlying kidney pathology, reluctance regarding timely histologic assessment in T2D patients with CKD should be critically questioned. As the etiology of CKD in T2D is heterogeneous, we aim to assess the prevalence and clinical disease course of typical diabetic vs atypical/non-specific vs non-diabetic vs coexisting kidney pathologies among T2D patients with mild-to-moderate kidney impairment [KDIGO stage G3a/A1-3 or G2/A2-3; i.e. estimated glomerular filtration rate (eGFR) 59-45 mL/min irrespective of albuminuria or eGFR 89-60 mL/min and albuminuria >30 mg/g creatinine].

Methods

The Innsbruck Diabetic Kidney Disease Cohort (IDKDC) study aims to enroll at least 65 T2D patients with mild-to-moderate kidney impairment to undergo a diagnostic kidney biopsy. Six-monthly clinical follow-ups for up to 5 years will provide clinical and laboratory data to assess cardio-renal outcomes. Blood, urine and kidney tissue specimen will be biobanked to identify diagnostic and prognostic biomarkers.

Conclusions

While current risk assessment is primarily based on clinical parameters, our study will provide the scientific background for a potential change of the diagnostic standard towards routine kidney biopsy and clarify its role for individual risk prediction regarding cardio-renal outcome in T2D patients with mild-to-moderate kidney impairment.

Renin-Angiotensin-Aldosterone System: From History to Practice of a Secular Topic

Renin-angiotensin-aldosterone system (RAAS) inhibitors are standard care in patients with hypertension, heart failure or chronic kidney disease (CKD). Although we have studied the RAAS for decades, there are still circumstances that remain unclear. In this review, we describe the evolution of the RAAS and pose the question of whether this survival trait is still necessary to humankind in the present age. We elucidate the benefits on cardiovascular health and kidney disease of RAAS inhibition and present promising novel medications. Furthermore, we address why more studies are needed to establish a new standard of care away from generally prescribing ACEi or ARB toward an improved approach to combine drugs tailored to the needs of individual patients.

SGLT2 Inhibitors in the Treatment of Diabetic Kidney Disease: More than Just Glucose Regulation

Diabetic kidney disease (DKD) is a severe and common complication and affects a quarter of patients with type 2 diabetes mellitus (T2DM). Oxidative stress and inflammation related to hyperglycemia are interlinked and contribute to the occurrence of DKD. It was shown that sodium-glucose cotransporter-2 (SGLT2) inhibitors, a novel yet already widely used therapy, may prevent the development of DKD and alter its natural progression. SGLT2 inhibitors induce systemic and glomerular hemodynamic changes, provide metabolic advantages, and reduce inflammatory and oxidative stress pathways. In T2DM patients, regardless of cardiovascular diseases, SGLT2 inhibitors may reduce albuminuria, progression of DKD, and doubling of serum creatinine levels, thus lowering the need for kidney replacement therapy by over 40%. The molecular mechanisms behind these beneficial effects of SGLT2 inhibitors extend beyond their glucose-lowering effects. The emerging studies are trying to explain these mechanisms at the genetic, epigenetic, transcriptomic, and proteomic levels.

Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study

BACKGROUND

Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory.

METHODS

We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation.

RESULTS

The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline.

CONCLUSIONS

Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.

Circulating thrombospondin-2 level for identifying individuals with rapidly declining kidney function trajectory in type 2 diabetes: a prospective study of the Hong Kong West Diabetes Registry

BACKGROUND

Thrombospondin-2 (TSP2) is a matricellular protein with tissue expression induced by hyperglycaemia. TSP2 has been implicated in non-diabetic renal injury in preclinical studies and high circulating levels were associated with worse kidney function in cross-sectional clinical studies. Therefore, we investigated the prospective associations of circulating TSP2 level with kidney function decline and the trajectories of estimated glomerular filtration rate (eGFR) in type 2 diabetes.

METHODS

Baseline serum TSP2 level was measured in 5471 patients with type 2 diabetes to evaluate its association with incident eGFR decline, defined as ≥ 40% sustained eGFR decline, using multivariable Cox regression analysis. Among participants with relatively preserved kidney function (Baseline eGFR ≥ 60 ml/min/1.73m2), joint latent class modelling was employed to identify three different eGFR trajectories. Their associations with baseline serum TSP2 was evaluated using multinomial logistic regression analysis. The predictive performance of serum TSP2 level was examined using time-dependent c-statistics and calibration statistics.

RESULTS

Over a median follow-up of 8.8 years, 1083 patients (19.8%) developed eGFR decline. Baseline serum TSP2 level was independently associated with incident eGFR decline (HR 1.21, 95%CI 1.07-1.37, P = 0.002). With internal validation, incorporating serum TSP2 to a model of clinical risk factors including albuminuria led to significant improvement in c-statistics from 83.9 to 84.4 (P < 0.001). Among patients with eGFR ≥ 60 ml/min/1.73m2, baseline serum TSP2 level was independently associated with a rapidly declining eGFR trajectory (HR 1.63, 95%CI 1.26-2.10, P < 0.001).

CONCLUSION

Serum TSP2 level was independently associated with incident eGFR decline, particularly a rapidly declining trajectory, in type 2 diabetes.

HucMSC exosomes attenuate partial bladder outlet obstruction-induced renal injury and cell proliferation via the Wnt/β-catenin pathway

Bladder outlet obstruction (BOO) can cause serious complications including kidney damage; nevertheless, there are currently no animal models for studying BOO-induced kidney damage. Mesenchymal stem cells (MSCs) are widely used in therapeutic studies of renal fibrosis. However, MSC-derived exosomes show improved safety profile and more controllable characteristics compared with those of MSCs. Herein, we established a kidney injury mouse model of partial bladder outlet obstruction (PBOO) and evaluated the effects of human umbilical cord MSC-derived exosomes (hucMSC-Exos) on PBOO-induced reflux kidney injury in this model. Exosomes were isolated from a hucMSC-conditioned medium, purified by ultracentrifugation, and examined. Living image was performed to indicate the distribution of hucMSC-Exos. The PBOO-treated mice interacted with PBS (phosphate-buffered saline) or hucMSC-Exos. Morphologic changes and expression of interstitial-fibrosis-related, cell proliferation and Wnt/β-catenin signaling-pathway indices were evaluated. At 7 days after induction of PBOO, structural destruction of renal tubules was observed. Expression of the interstitial markers and the cellular-proliferation index increased significantly in the PBOO group compared with the control group (p < 0.05). The isolated exosomes were 30-150 nm in diameter, showing a round shape and bilayer membrane structure with CD63, TSG101, Alix expressed, enriched in the kidney of the PBOO group. Administering hucMSC-Exos to post-PBOO mice reversed renal injury and suppressed expression of Wnt/β-catenin signaling pathway-related proteins. hucMSC-Exos inhibited PBOO-induced kidney injury and cellular proliferation and suppressed the Wnt/β-catenin signaling pathway. Our findings will spur the development of novel hucMSC-Exo-mediated therapies for treating patients with renal fibrosis.

Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease

Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor-1 and -2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule-1, beta-2-microglobulin and neutrophil gelatinase-associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.

REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD.

Trajectories of kidney function in diabetes: a clinicopathological update

Diabetic nephropathy has been traditionally diagnosed based on persistently high albuminuria and a subsequent decline in glomerular filtration rate (GFR), which is widely recognized as the classical phenotype of diabetic kidney disease (DKD). Several studies have emphasized that trajectories of kidney function in patients with diabetes (specifically, changes in GFR and albuminuria over time) can differ from this classical DKD phenotype. Three alternative DKD phenotypes have been reported to date and are characterized by albuminuria regression, a rapid decline in GFR, or non-proteinuric or non-albuminuric DKD. Although kidney biopsies are not typically required for the diagnosis of DKD, a few studies of biopsy samples from patients with DKD have demonstrated that changes in kidney function associate with specific histopathological findings in diabetes. In addition, various clinical and biochemical parameters are related to trajectories of GFR and albuminuria. Collectively, pathological and clinical characteristics can be used to predict trajectories of GFR and albuminuria in diabetes. Furthermore, cohort studies have suggested that the risks of kidney and cardiovascular outcomes might vary among different phenotypes of DKD. A broader understanding of the clinical course of DKD is therefore crucial to improve risk stratification and enable early interventions that prevent adverse outcomes.

Multi-omics approaches for revealing the complexity of cardiovascular disease

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

Novel biomarkers of diabetic kidney disease: current status and potential clinical application

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD). Although both albuminuria and glomerular filtration rate (GFR) are well-established diagnostic/prognostic biomarkers of DKD, they have important limitations. There is, thus, increasing quest to find novel biomarkers to identify the disease in an early stage and to improve risk stratification. In this review, we will outline the major pitfalls of currently available markers, describe promising novel biomarkers, and discuss their potential clinical relevance. In particular, we will focus on the importance of recent advancements in multi-omic technologies in the discovery of new DKD biomarkers. In addition, we will provide an update on new emerging approaches to explore renal function and structure, using functional tests and imaging.

Rational selection of a biomarker panel targeting unmet clinical needs in kidney injury

The pipeline of biomarker translation from bench to bedside is challenging and limited biomarkers have been adopted to routine clinical care. Ideally, biomarker research and development should be driven by unmet clinical needs in health care. To guide researchers, clinical chemists and clinicians in their biomarker research, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has developed a structured questionnaire in which the clinical gaps in current clinical pathways are identified and desirable performance specifications are predefined. In kidney injury, the high prevalence of the syndrome acute kidney injury (AKI) in the hospital setting has a significant impact on morbidity, patient survival and health care costs, but the use of biomarkers indicating early kidney injury in daily patient care remains limited. Routinely, medical labs measure serum creatinine, which is a functional biomarker, insensitive for detecting early kidney damage and cannot distinguish between renal and prerenal AKI. The perceived unmet clinical needs in kidney injury were identified through the EFLM questionnaire. Nephrologists within our tertiary care hospital emphasized that biomarkers are needed for (1) early diagnosis of in-hospital AKI after a medical insult and in critically ill patients, (2) risk stratification for kidney injury prior to a scheduled (elective) intervention, (3) kidney injury monitoring in patients scheduled to receive nephrotoxic medication and after kidney transplantation and (4) differentiation between prerenal AKI and structural kidney damage. The biomarker search and selection strategy resulted in a rational selection of an eleven-protein urinary panel for kidney injury that target these clinical needs. To assess the clinical utility of the proposed biomarker panel in kidney injury, a multiplexed LC-MS test is now in development for the intended translational research.

Kidney Diseases: The Age of Molecular Markers

Kidney diseases are conditions that increase the morbidity and mortality of those afflicted. Diagnosis of these conditions is based on parameters such as the glomerular filtration rate (GFR), measurement of serum and urinary creatinine levels and equations derived from these measurements (Wasung, Chawla, Madero. Clin Chim Acta 438:350-357, 2015). However, serum creatinine as a marker for measuring renal dysfunction has its limitations since it is altered in several other physiological situations, such as in patients with muscle loss, after intense physical exercise or in people on a high protein diet (Riley, Powers, Welch. Res Q Exerc Sport 52(3):339-347, 1981; Juraschek, Appel, Anderson, Miller. Am J Kidney Dis 61(4):547-554, 2013). Besides the fact that serum creatinine is a marker that indicates glomerular damage, it is necessary the discovery of new biomarkers that reflect not only glomerular damage but also tubular impairment. Recent advances in Molecular Biology have led to the generation or identification of new biomarkers for kidney diseases such as: Acute Kidney Failure (AKI), chronic kidney disease (CKD), nephritis or nephrotic syndrome. There are recent markers that have been used to aid in diagnosis and have been shown to be more sensitive and specific than classical markers, such as neutrophil gelatinase associated lipocalin (NGAL) or kidney injury molecule-1 (KIM-1) (Wasung, Chawla, Madero. Clin Chim Acta 438:350-357, 2015; George, Gounden. Adv Clin Chem 88:91-119, 2019; Han, Bailly, Abichandani, Thadhani, Bonventre. Kidney Int 62(1):237-244, 2002; Fontanilla, Han. Expert Opin Med Diagn 5(2):161-173, 2011). However, early diagnostic biomarkers are still necessary to assist the intervention and monitor of the progression of these conditions.

Biomarkers of Inflammation and Glomerular Filtration Rate in Individuals with Recent-Onset Type 1 and Type 2 Diabetes

CONTEXT

While inflammation has been associated with kidney function in long-standing diabetes, its possible association in newly diagnosed diabetes is unknown.

OBJECTIVE

To investigate cross-sectional and prospective associations between biomarkers of inflammation and kidney function in recent-onset diabetes.

METHODS

The study included individuals with type 1 and type 2 diabetes with known diabetes duration of <1 year from the German Diabetes Study. Baseline serum concentrations of 74 biomarkers were measured using proximity extension assay technology and their associations with estimated glomerular filtration rate (eGFR) and kidney function decline over 5 years were tested using multiple linear and logistic regression analysis.

RESULTS

The cross-sectional analysis included 165 individuals with type 1 diabetes and 291 with type 2 diabetes. Baseline eGFR was higher in type 1 compared with type 2 diabetes (102 ± 15 vs 90 ± 16 mL/min/1.73 m2; P < 0.0001). After full adjustment for covariates and multiple testing, 7 biomarkers were associated with lower baseline eGFR in type 1 diabetes and 24 were associated with lower baseline eGFR in type 2 diabetes. Among these biomarkers, 6 biomarkers (CD5, CCL23, CST5, IL-10RB, PD-L1, TNFRSF9) were inversely associated with eGFR in both diabetes types. The prospective analysis did not detect associations between inflammatory biomarkers and kidney function decline. No evidence of an interaction between diabetes type and inflammatory biomarkers was found.

CONCLUSION

Several biomarkers of inflammation associate with lower baseline eGFR in recent-onset type 1 and type 2 diabetes, but do not associate with kidney function loss during the first 5 years after the diagnosis of diabetes.

Intra-individual variability of eGFR trajectories in early diabetic kidney disease and lack of performance of prognostic biomarkers

Studies reporting on biomarkers aiming to predict adverse renal outcomes in patients with type 2 diabetes and kidney disease (DKD) conventionally define a surrogate endpoint either as a percentage of decrease of eGFR (e.g. ≥ 30%) or an absolute decline (e.g. ≥ 5 ml/min/year). The application of those study results in clinical practise however relies on the assumption of a linear and intra-individually stable progression of DKD. We studied 860 patients of the PROVALID study and 178 of an independent population with a relatively preserved eGFR at baseline and at least 5 years of follow up. Individuals with a detrimental prognosis were identified using various thresholds of a percentage or absolute decline of eGFR after each year of follow up. Next, we determined how many of the patients met the same criteria at other points in time. Interindividual eGFR decline was highly variable but in addition intra-individual eGFR trajectories also were frequently non-linear. For example, of all subjects reaching an endpoint defined as a decrease of eGFR by ≥ 30% between baseline and 3 years of follow up, only 60.3 and 45.2% lost at least the same amount between baseline and year 4 or 5. The results were similar when only patients on stable medication or subpopulations based on baseline eGFR or albuminuria status were analyzed or an eGFR decline of ≥ 5 ml/min/1.73m²/year was used. Identification of reliable biomarkers predicting adverse prognosis is a strong clinical need given the large interindividual variability of DKD progression. However, it is conceptually challenging in early DKD because of non-linear intra-individual eGFR trajectories. As a result, the performance of a prognostic biomarker may be accurate after a specific time of follow-up in a single population only.

A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes

BACKGROUND

Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict.

METHODS

We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics" studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques.

RESULTS

At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001).

CONCLUSION

Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.

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.

Mannitol and renal graft injury in patients undergoing deceased donor renal transplantation - a randomized controlled clinical trial

Background

Ischaemia/reperfusion (I/R) injury is associated with renal tissue damage during deceased donor renal transplantation. The effect of mannitol to reduce I/R injury during graft reperfusion in renal transplant recipients is based on weak evidence. We evaluated the effect of mannitol to reduce renal graft injury represented by 16 serum biomarkers, which are indicators for different important pathophysiological pathways. Our primary outcome were differences in biomarker concentrations between the mannitol and the placebo group 24 h after graft reperfusion. Additionally, we performed a linear mixed linear model to account biomarker concentrations before renal transplantation.

Methods

Thirty-four patients undergoing deceased donor renal transplantation were randomly assigned to receive either 20% mannitol or 0.9% NaCl placebo solution before, during, and after graft reperfusion. Sixteen serum biomarkers (MMP1, CHI3L1, CCL2, MMP8, HGF, GH, FGF23, Tie2, VCAM1, TNFR1, IGFBP7, IL18, NGAL, Endostatin, CystC, KIM1) were measured preoperatively and 24 h after graft reperfusion using Luminex assays and ELISA.

Results

Sixteen patients in each group were analysed. Tie2 differed 24 h after graft reperfusion between both groups (p = 0.011). Change of log2 transformed concentration levels over time differed significantly in four biomarkers (VCAM1,Endostatin, KIM1, GH; p = 0.007; p = 0.013; p = 0.004; p = 0.033; respectively) out of 16 between both groups.

Conclusion

This study showed no effect of mannitol on I/R injury in patients undergoing deceased renal transplantation. Thus, we do not support the routinely use of mannitol to attenuate I/R injury.

Trial registration

NCT02705573. Registered on 10th March 2016.

Novel Biomarkers for Change in Renal Function in People With Dysglycemia

OBJECTIVE

Diabetes is a major risk factor for renal function decline and failure. The availability of multiplex panels of biochemical markers provides the opportunity to identify novel biomarkers that can better predict changes in renal function than routinely available clinical markers.

RESEARCH DESIGN AND METHODS

The concentration of 239 biochemical markers was measured in stored serum from participants in the biomarker substudy of Outcome Reduction With Initial Glargine Intervention (ORIGIN) trial. Repeated-measures mixed-effects models were used to compute the annual change in eGFR (measured as mL/min/1.73 m²/year) for the 7,482 participants with a recorded baseline and follow-up eGFR. Linear regression models using forward selection were used to identify the independent biomarker determinants of the annual change in eGFR after accounting for baseline HbA_1c, baseline eGFR, and routinely measured clinical risk factors. The incidence of the composite renal outcome (i.e., renal replacement therapy, renal death, renal failure, albuminuria progression, doubling of serum creatinine) and death within each fourth of change in eGFR predicted from these models was also estimated.

RESULTS

During 6.2 years of median follow-up, the median annual change in eGFR was -0.18 mL/min/1.73 m²/year. Fifteen biomarkers independently predicted eGFR decline after accounting for cardiovascular risk factors, as did 12 of these plus 1 additional biomarker after accounting for renal risk factors. Every 0.1 mL/min/1.73 m² predicted annual fall in eGFR predicted a 13% (95% CI 12, 14%) higher mortality.

CONCLUSIONS

Adding up to 16 biomarkers to routinely measured clinical risk factors improves the prediction of annual change in eGFR in people with dysglycemia.

Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques

Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was intended to identify such factors using ML. The longitudinal data were stratified by time after patient enrollment to differentiate early and late predictors. Our results showed that Random Forest and Simple Logistic Regression methods exhibited the best performance among the evaluated algorithms. Baseline values for glomerular filtration rate (GFR), urinary creatinine, urinary albumin, potassium, cholesterol, low-density lipoprotein, and urinary albumin to creatinine ratio were identified as DN predictors. Early predictors were the baseline values of GFR, systolic blood pressure, as well as fasting plasma glucose (FPG) and potassium at month 4. Changes per year in GFR, FPG, and triglycerides were recognized as predictors of late development. In conclusion, ML-based methods successfully identified predictive factors for DN among patients with T2DM.

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m², urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.

Canagliflozin reduces inflammation and fibrosis biomarkers: a potential mechanism of action for beneficial effects of SGLT2 inhibitors in diabetic kidney disease

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.

Identification of dicarbonyl and L-xylulose reductase as a therapeutic target in human chronic kidney disease

An imbalance of nephroprotective factors and renal damaging molecules contributes to development and progression of chronic kidney disease (CKD). We investigated associations of renoprotective factor gene expression patterns with CKD severity and outcome. Gene expression profiles of 197 previously reported renoprotective factors were analyzed in a discovery cohort in renal biopsies of 63 CKD patients. Downregulation of dicarbonyl and L-xylulose reductase (DCXR) showed the strongest association with disease progression. This significant association was validated in an independent set of 225 patients with nephrotic syndrome from the multicenter NEPTUNE cohort. Reduced expression of DCXR was significantly associated with degree of histological damage as well as with lower estimated glomerular filtration rate and increased urinary protein levels. DCXR downregulation in CKD was confirmed in 3 publicly available transcriptomics data sets in the context of CKD. Expression of DCXR showed positive correlations to enzymes that are involved in dicarbonyl stress detoxification based on transcriptomics profiles. The sodium glucose cotransporter-2 (SGLT2) inhibitors canagliflozin and empagliflozin showed a beneficial effect on renal proximal tubular cells under diabetic stimuli-enhanced DCXR gene expression. In summary, lower expression of the renoprotective factor DCXR in renal tissue is associated with more severe disease and worse outcome in human CKD.

Matrix Metalloproteinases in Renal Diseases: A Critical Appraisal

Matrix metalloproteinases (MMPs) are endopeptidases within the metzincin protein family that not only cleave extracellular matrix (ECM) components, but also process the non-ECM molecules, including various growth factors and their binding proteins. MMPs participate in cell to ECM interactions, and MMPs are known to be involved in cell proliferation mechanisms and most probably apoptosis. These proteinases are grouped into six classes: collagenases, gelatinases, stromelysins, matrilysins, membrane type MMPs, and other MMPs. Various mechanisms regulate the activity of MMPs, inhibition by tissue inhibitors of metalloproteinases being the most important. In the kidney, intrinsic glomerular cells and tubular epithelial cells synthesize several MMPs. The measurement of circulating MMPs can provide valuable information in patients with kidney diseases. They play an important role in many renal diseases, both acute and chronic. This review attempts to summarize the current knowledge of MMPs in the kidney and discusses recent data from patient and animal studies with reference to specific diseases. A better understanding of the MMPs' role in renal remodeling may open the way to new interventions favoring deleterious renal changes in a number of kidney diseases.

Albuminuric and non-albuminuric patterns of chronic kidney disease in type 2 diabetes

A growing body of evidence supports a shift in the natural history of chronic kidney disease (CKD) in subjects with diabetes. Specifically, normoalbuminuric chronic kidney disease (NA-CKD), which is characterized by a decline in the glomerular filtration rate in the absence of a preceding or accompanying elevation of albuminuria, has become a widely prevalent variant of renal impairment in diabetes. Diabetic women and nonsmoking individuals with better glycemic control have a better chance of preserving normoalbuminuria, even in the case of declining renal function. The wide use of renin-angiotensin system blockers, advances in antihyperglycemic, antihypertensive, and hypolipidemic therapy, and smoking cessation are suspected to be responsible for an increasing proportion of NA-CKD among diabetic subjects with renal impairment. Significant differences in the sets of risk factors, renal morphology, comorbidity, and outcomes were found between the albuminuric and normoalbuminuric CKD patterns. NA-CKD, even if a more favorable option in terms of the risk of end-stage renal disease, is clearly associated with cardiovascular disease and its risk factors. The presence of NA-CKD in patients with diabetes increases the risk of myocardial infarction, stroke, and cardiovascular death. The study of the molecular pathways, clinical course, and outcomes of NA-CKD in diabetic subjects and the search for more specific diagnostic and treatment options are challenges for future research.

Multimarker Panels in Diabetic Kidney Disease: The Way to Improved Clinical Trial Design and Clinical Practice?

Diabetic kidney disease (DKD) is a complex and multifactorial disorder associated with deregulations in a large number of different biological pathways on the molecular level. Using the 2 established biomarkers, estimated glomerular filtration rate (eGFR) and albuminuria will not allow allocating patients to tailored therapy. Molecular multimarker panels as sensors for the deregulation of the various disease mechanisms combined with a better understanding of how investigational as well as approved drugs interfere with these disease processes forms the basis for platform trials in DKD. In these platform trials, patients with DKD are assigned to the most suitable treatment arm based on their molecular marker profile. Close monitoring of biomarkers after treatment initiation together with assessment of renal function and "off-target" effects will allow identification of therapy responders, with nonresponders shifted to the next-best treatment arm based on their molecular profile. In this viewpoint article, we summarize evidence on the variation of DKD disease progression as well as the response to therapy and outline procedures to model disease pathophysiology supporting biomarker panel construction. Finally, the use of biomarkers in clinical trial setup is discussed.

Normoalbuminuric chronic kidney disease in diabetes

Urinary albumin excretion (UAE) is widely used in clinical practice as indicator of diabetic kidney disease. According to the classical concept of the natural course of diabetic nephropathy, an increase in UAE usually precedes a decline in renal function. Meanwhile, a growing body of evidences indicates a high prevalence of normoalbuminuric chronic kidney disease (NA-CKD) in diabetic subjects, especially among patients with type 2 diabetes. An increase in NA-CKD prevalence can be results of improved glucose, blood pressure, and lipid control, widespread use of renin-angiotensin system blockers, and smoking cessation. It was shown that NA-CKD is more prevalent among women and is associated with arterial hypertension and coronary artery disease. The renal structure in subjects with NA-CKD is more heterogeneous when compared to patients with increased albuminuria, wherein interstitial changes and arteriolosclerosis could be the principal morphological findings, while signs of glomerulopathy may be absent. The prognostic value of NA-CKD needs to be clarified. It was shown that NA-CKD increases the risk of myocardial infarction, stroke and cardiovascular death in patients with diabetes. The search for alternative diagnostic markers for detecting of diabetic kidney disease in the absence of albuminuria, is of practical importance. The evaluations of the markers of tubular damage and interstitial fibrosis, as well as proteomic approaches, are considered as perspective diagnostic and prognostic options in NA-CKD. The study of pathogenesis, pathology, clinical course of NA-CKD in diabetic patients, as well as the development of more specific diagnostic and treatment options is a challenge for future research.

Molecular, histological, and clinical phenotyping of diabetic nephropathy: valuable complementary information?

The incidence and prevalence of diabetic kidney disease is increasing. Observational and interventional studies suggest that the pathophysiology varies between individuals and within a patient over time. There is a huge clinical need to describe the molecular processes that modulate diabetic kidney disease. "Omics" experiments combined with bioinformatical analysis tools might allow for profiling of patients on an individual or at least group level to improve prediction of prognosis and guide targeted therapy.

Validation of Plasma Biomarker Candidates for the Prediction of eGFR Decline in Patients With Type 2 Diabetes

OBJECTIVE

The decline of estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes is variable, and early interventions would likely be cost-effective. We elucidated the contribution of 17 plasma biomarkers to the prediction of eGFR loss on top of clinical risk factors.

RESEARCH DESIGN AND METHODS

We studied participants in PROVALID (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers), a prospective multinational cohort study of patients with type 2 diabetes and a follow-up of more than 24 months (n = 2,560; baseline median eGFR, 84 mL/min/1.73 m²; urine albumin-to-creatinine ratio, 8.1 mg/g). The 17 biomarkers were measured at baseline in 481 samples using Luminex and ELISA. The prediction of eGFR decline was evaluated by linear mixed modeling.

RESULTS

In univariable analyses, 9 of the 17 markers showed significant differences in median concentration between stable and fast-progressing patients. A linear mixed model for eGFR obtained by variable selection exhibited an adjusted R² of 62%. A panel of 12 biomarkers was selected by the procedure and accounted for 34% of the total explained variability, of which 32% was due to 5 markers. The individual contribution of each biomarker to the prediction of eGFR decline on top of clinical predictors was generally low. When included into the model, baseline eGFR exhibited the largest explained variability of eGFR decline (R² of 79%), and the contribution of each biomarker dropped below 1%.

CONCLUSIONS

In this longitudinal study of patients with type 2 diabetes and maintained eGFR at baseline, 12 of the 17 candidate biomarkers were associated with eGFR decline, but their predictive power was low.

The Role of Systems Biologic Approach in Cell Signaling and Drug Development Responses-A Mini Review

The immune system is an integral aspect of the human defense system and is primarily responsible for and involved in the communication between the immune cells. It also plays an important role in the protection of the organism from foreign invaders. Recent studies in the literature have described its role in the process of hematopoiesis, lymphocyte recruitment, T cell subset differentiation and inflammation. However, the specific molecular mechanisms underlying these observations remain elusive, impeding the elaborate manipulation of cytokine sequential delivery in tissue repair. Previously, the discovery of new drugs and systems biology went hand in hand; although Systems biology as a term has only originated in the last century. Various new chemicals were tested on the human body, and studied through observation. Animal models replaced humans for initial trials, but the interactions, response, dose and effect between animals and humans could not be directly correlated. Therefore, there is a need to form disease models outside of human subjects to check the effectiveness and response of the newer natural or synthetic chemicals. These emulate human disease conditions wherein the behavior of the chemicals would be similar in the disease model and humans.

Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function

Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.

Biomarkers of diabetic kidney disease

Diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in diabetes. The quest for both prognostic and surrogate endpoint biomarkers for advanced DKD and end-stage renal disease has received major investment and interest in recent years. However, at present no novel biomarkers are in routine use in the clinic or in trials. This review focuses on the current status of prognostic biomarkers. First, we emphasise that albuminuria and eGFR, with other routine clinical data, show at least modest prediction of future renal status if properly used. Indeed, a major limitation of many current biomarker studies is that they do not properly evaluate the marginal increase in prediction on top of these routinely available clinical data. Second, we emphasise that many of the candidate biomarkers for which there are numerous sporadic reports in the literature are tightly correlated with each other. Despite this, few studies have attempted to evaluate a wide range of biomarkers simultaneously to define the most useful among these correlated biomarkers. We also review the potential of high-dimensional panels of lipids, metabolites and proteins to advance the field, and point to some of the analytical and post-analytical challenges of taking initial studies using these and candidate approaches through to actual clinical biomarker use.

The Promise of Systems Biology for Diabetic Kidney Disease

Diabetic kidney disease (DKD) has a complex and prolonged pathogenesis involving many cell types in the kidney as well as extrarenal factors. It is clinically silent for many years after the onset of diabetes and usually progresses over decades. Given this complexity, a comprehensive and unbiased molecular approach is best suited to help identify the most critical mechanisms responsible for progression of DKD and those most suited for targeted intervention. Systems biological investigations provide such an approach since they examine the entire network of molecular changes that occur in a disease process in a comprehensive way instead of focusing on a single abnormal molecule or pathway. Systems biological studies can also start with analysis of the disease in humans, not in animal or cell culture models that often poorly reproduce the changes in human DKD. Indeed, in the last decade, systems biological approaches have led to the identification of critical molecular abnormalities in DKD and have directly led to development of new biomarkers and potential treatments for DKD.

Establishing the presence or absence of chronic kidney disease: Uses and limitations of formulas estimating the glomerular filtration rate

The development of formulas estimating glomerular filtration rate (eGFR) from serum creatinine and cystatin C and accounting for certain variables affecting the production rate of these biomarkers, including ethnicity, gender and age, has led to the current scheme of diagnosing and staging chronic kidney disease (CKD), which is based on eGFR values and albuminuria. This scheme has been applied extensively in various populations and has led to the current estimates of prevalence of CKD. In addition, this scheme is applied in clinical studies evaluating the risks of CKD and the efficacy of various interventions directed towards improving its course. Disagreements between creatinine-based and cystatin-based eGFR values and between eGFR values and measured GFR have been reported in various cohorts. These disagreements are the consequence of variations in the rate of production and in factors, other than GFR, affecting the rate of removal of creatinine and cystatin C. The disagreements create limitations for all eGFR formulas developed so far. The main limitations are low sensitivity in detecting early CKD in several subjects, e.g., those with hyperfiltration, and poor prediction of the course of CKD. Research efforts in CKD are currently directed towards identification of biomarkers that are better indices of GFR than the current biomarkers and, particularly, biomarkers of early renal tissue injury.