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

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.

Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.

Mitochondrial DNA - novel mechanisms of kidney damage and potential biomarker

PURPOSE OF REVIEW

MtDNA copy number (CN), a putative noninvasive biomarker of mitochondrial dysfunction, is associated with renal disease. The purpose of this review is to describe studies which measured human blood mtDNA-CN in the context of chronic kidney disease (CKD), and to evaluate its potential as a clinical biomarker of kidney disease.

RECENT FINDINGS

Following on from small scale cross-sectional studies implicating mtDNA-CN changes in diabetic kidney disease, recent large scale population studies provide compelling evidence of the association of mtDNA-CN and risk of renal disease in the general population and poor outcomes in CKD patients.

SUMMARY

The kidney has high bioenergetic needs, renal cells are rich in mitochondrial content containing 100s to 1000s of mtDNA molecular per cell. MtDNA has emerged as both a potential mediator, and a putative biomarker of renal disease. Damage to mtDNA can result in bioenergetic deficit, and reduced MtDNA levels in the blood have been shown to correlate with CKD. Furthermore, leakage of mtDNA outside of mitochondria into the cytosol/periphery can directly cause inflammation and is implicated in acute kidney injury (AKI). Recent large-scale population studies show the association of mtDNA-CN and renal disease and provide a strong basis for the future evaluation of circulating DNA-CN in longitudinal studies to determine its utility as a clinical biomarker for monitoring renal function.

Precision medicine in regulatory decision making: Biomarkers used for patient selection in European Public Assessment Reports from 2018 to 2020

Biomarkers can guide precision medicine in clinical trials and practice. They can increase clinical trials' efficiency through selection of study populations more likely to benefit from treatment, thus increasing statistical power and reducing sample size requirements or study duration. We performed a narrative synthesis to explore biomarker utilization for patient selection to guide precision medicine trials in marketing authorization dossiers of centrally approved medicines in Europe between 2018 and 2020 and analyzed in-depth those that eventually included biomarkers in the medicines' indications. From 119 eligible products, 26 included a biomarker in the indication, of which most were oncology products (n = 15). Included biomarkers were often known from literature or from previously approved products in the European Union or the United States. Additionally, 52 dossiers mentioned one or more biomarkers for patient selection in their clinical efficacy and safety information. Although these were not always included in the medicines' indication, they were often implicitly embedded in condition definitions adopted from clinical guidelines or practice. In 15 out of the 26 medicines with a biomarker-guided indication, only biomarker-positive populations were included in the main clinical studies supporting the marketing authorization. These studies were mostly randomized controlled trials or single-arm trials; only two products were studied for multiple indications in an innovative basket trial. Definitions of biomarkers could be subject of debate and needed adaptation after post hoc analyses requested by the assessment committee in four cases, stressing the importance of thorough justification of these definitions to include the right population for an optimal benefit-risk balance, enabling precise medicine.

Urinary and Kidney Podocalyxin and Podocin Levels in Diabetic Kidney Disease: A Kidney Biopsy Study

Rationale & Objective

Diabetic kidney diseases (DKDs) are the most common cause of dialysis-dependent kidney disease around the world. Previous studies have suggested that urinary level of podocyte-associated molecules may predict the prognosis of DKD.

Study Design

Observational cohort.

Setting & Participants

118 consecutive patients with biopsy-proven DKD; 13 nondiabetic patients with hypertensive nephrosclerosis as controls.

Predictors

Urinary podocalyxin and podocin levels were obtained by quantitative polymerase chain reaction and enzyme-linked immunosorbent assay (ELISA) and the corresponding intrarenal levels by western blotting.

Outcomes

Dialysis-free survival; kidney event-free survival; rate of kidney function decline in 12 months.

Analytical Approach

Correlation and time to event analysis.

Results

Urinary podocalyxin level was closely correlated with its messenger RNA (mRNA) level (r = 0.562, P < 0.001), but this did not predict the progression of DKD. Intrarenal podocalyxin level had only modest correlation with its urinary mRNA and ELISA levels, was an independent predictor of dialysis-free survival (adjusted HR, 1.85; 95% CI, 1.21-2.82; P = 0.005), and showed an insignificant trend of predicting kidney event-free survival (adjusted HR, 1.36; 95% CI, 0.94-1.95; P = 0.10). Urinary podocin level by ELISA had a modest correlation with the rate of kidney function decline (r = 0.238, P = 0.01) but did not predict dialysis-free survival.

Limitations

Small sample size; lack of serial measurement.

Conclusions

Intrarenal podocalyxin level, but not its urinary level, was an independent predictor of dialysis-free survival, whereas urinary podocin level by ELISA correlated with the rate of kidney function decline. Although intrarenal podocalyxin level has prognostic value, it may not be suitable for routine clinical use.

Precision Nephrology in Patients with Diabetes and Chronic Kidney Disease

Diabetes is the leading cause of kidney failure and specifically, diabetic kidney disease (DKD) occurs in up to 30% of all diabetic patients. Kidney disease attributed to diabetes is a major contributor to the global burden of the disease in terms of clinical and socio-economic impact, not only because of the risk of progression to End-Stage Kidney Disease (ESKD), but also because of the associated increase in cardiovascular (CV) risk. Despite the introduction of novel treatments that allow us to reduce the risk of future outcomes, a striking residual cardiorenal risk has been reported. This risk is explained by both the heterogeneity of DKD and the individual variability in response to nephroprotective treatments. Strategies that have been proposed to improve DKD patient care are to develop novel biomarkers that classify with greater accuracy patients with respect to their future risk (prognostic) and biomarkers that are able to predict the response to nephroprotective treatment (predictive). In this review, we summarize the principal prognostic biomarkers of type 1 and type 2 diabetes and the novel markers that help clinicians to individualize treatments and the basis of the characteristics that predict an optimal response.

Monocyte chemoattractant protein-1 predicts the development of diabetic nephropathy

AIM

Diabetic nephropathy (DN) is a devastating complication of diabetes mellitus (DM). Therefore, screening strategies in order to prevent its development and/or retard its progression are of paramount importance. We investigated if monocyte chemoattractant protein-1 (MCP-1) was associated with new onset microalbuminuria-the earliest sign of the albuminuric phenotype of DN- in patients with type 2 DM and normoalbuminuria.

METHODS

We measured MCP-1 in serum and urine samples from patients of the Randomized Olmesartan And Diabetes Microalbuminuria Prevention (ROADMAP) study and its Observational Follow-up (OFU) cohort. A case control design was used with inclusion of 172 patients who developed microalbuminuria (MA) and of 188 well matched controls who remained normoalbuminuric.

RESULTS

The median duration of follow-up for the ROADMAP cohorts was 6.5 years, whereas the mean time until occurrence of MA was 53.2 months. In the multivariate analysis, serum and urine MCP-1 remained significant predictors of new onset MA. The risk for MA increased continuously with increasing serum and urine MCP-1 levels but reached statistical significance only in the highest quartiles. The risk associations were stronger with serum MCP-1.

CONCLUSIONS

MCP-1 is a marker and possibly a mediator of early diabetic nephropathy. Further prospective studies are necessary to test whether diabetic patients with elevated MCP-1 levels would benefit from specific therapeutic interventions.

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

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.

Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative

Diabetes Mellitus is one of the World Health Organization's priority diseases under research by the first and second programmes of Innovative Medicines Initiative, with the acronyms IMI1 and IMI2, respectively. Up to October of 2019, 13 projects were funded by IMI for Diabetes & Metabolic disorders, namely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. In order to prompt a better integration of achievements between the different projects, we perform a literature review and used three data sources, namely the official project's websites, the contact with the project's coordinators and co-coordinator, and the CORDIS database. From the 662 citations identified, 185 were included. The data collected were integrated into the objectives proposed for the four IMI2 program research axes: (1) target and biomarker identification, (2) innovative clinical trials paradigms, (3) innovative medicines, and (4) patient-tailored adherence programmes. The IMI funded projects identified new biomarkers, medical and research tools, determinants of inter-individual variability, relevant pathways, clinical trial designs, clinical endpoints, therapeutic targets and concepts, pharmacologic agents, large-scale production strategies, and patient-centered predictive models for diabetes and its complications. Taking into account the scientific data produced, we provided a joint vision with strategies for integrating personalized medicine into healthcare practice. The major limitations of this article were the large gap of data in the libraries on the official project websites and even the Cordis database was not complete and up to date.

Current updates on protein as biomarkers for diabetic kidney disease: a systematic review

BACKGROUND

In the past decade, researchers have been focused on discovering protein biomarkers for diabetic kidney disease. This paper aims to search for, analyze, and synthesize current updates regarding the development of these efforts.

METHODS

We systematically searched the ScienceDirect, SpringerLink, and PubMed databases for observational studies of protein biomarkers in patients with diabetes mellitus. We included studies published between January 2018 and April 2020, that were based on a population of patients with type-1 or type-2 diabetes mellitus aged ⩾18 years, with an observational design such as cross-sectional, case-control, or cohort studies. The dependent variable of the research results was in the form of protein biomarkers from urine, plasma, or serum.

RESULTS

Following the screening process, 20 research articles with available full text met the inclusion criteria. These could be categorized as glomerular biomarkers (ANGPTL4, beta-2 microglobulin, Smad1, and glypican-5); inflammatory biomarkers (MCP-1 and adiponectin); and tubular biomarkers (NGAL, VDBP, megalin, sKlotho, and KIM-1). The development of a panel of biomarkers showed more promising results than those for a single biomarker in diagnosing diabetic kidney disease.

CONCLUSION

All the biomarkers discussed in this review showed promising results for predicting diabetic kidney disease because they correlate with albuminuria, eGFR, or both. However, of the 11 protein biomarkers, none have prognostic value beyond albuminuria and eGFR.

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.

Digital pathology and computational image analysis in nephropathology

The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.

The Role of Prognostic and Predictive Biomarkers for Assessing Cardiovascular Risk in Chronic Kidney Disease Patients

Chronic kidney disease (CKD) is currently defined as the presence of proteinuria and/or an eGFR < 60 mL/min/1.73m² on the basis of the renal diagnosis. The global dimension of CKD is relevant, since its prevalence and incidence have doubled in the past three decades worldwide. A major complication that occurs in CKD patients is the development of cardiovascular (CV) disease, being the incidence rate of fatal/nonfatal CV events similar to the rate of ESKD in CKD. Moreover, CKD is a multifactorial disease where multiple mechanisms contribute to the individual prognosis. The correct development of novel biomarkers of CV risk may help clinicians to ameliorate the management of CKD patients. Biomarkers of CV risk in CKD patients are classifiable as prognostic, which help to improve CV risk prediction regardless of treatment, and predictive, which allow the selection of individuals who are likely to respond to a specific treatment. Several prognostic (cystatin C, cardiac troponins, markers of inflammation, and fibrosis) and predictive (genes, metalloproteinases, and complex classifiers) biomarkers have been developed. Despite previous biomarkers providing information on the pathophysiological mechanisms of CV risk in CKD beyond proteinuria and eGFR, only a minority have been adopted in clinical use. This mainly depends on heterogeneous results and lack of validation of biomarkers. The purpose of this review is to present an update on the already assessed biomarkers of CV risk in CKD and examine the strategies for a correct development of biomarkers in clinical practice. Development of both predictive and prognostic biomarkers is an important task for nephrologists. Predictive biomarkers are useful for designing novel clinical trials (enrichment design) and for better understanding of the variability in response to the current available treatments for CV risk. Prognostic biomarkers could help to improve risk stratification and anticipate diagnosis of CV disease, such as heart failure and coronary heart disease.

Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease

Chronic kidney disease (CKD), defined as the presence of albuminuria and/or reduction in estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2, is considered a growing public health problem, with its prevalence and incidence having almost doubled in the past three decades. The implementation of novel biomarkers in clinical practice is crucial, since it could allow earlier diagnosis and lead to an improvement in CKD outcomes. Nevertheless, a clear guidance on how to develop biomarkers in the setting of CKD is not yet available. The aim of this review is to report the framework for implementing biomarkers in observational and intervention studies. Biomarkers are classified as either prognostic or predictive; the first type is used to identify the likelihood of a patient to develop an endpoint regardless of treatment, whereas the second type is used to determine whether the patient is likely to benefit from a specific treatment. Many single assays and complex biomarkers were shown to improve the prediction of cardiovascular and kidney outcomes in CKD patients on top of the traditional risk factors. Biomarkers were also shown to improve clinical trial designs. Understanding the correct ways to validate and implement novel biomarkers in CKD will help to mitigate the global burden of CKD and to improve the individual prognosis of these high-risk patients.

Urinary Amino-Terminal Pro–C-Type Natriuretic Peptide: A Novel Marker of Chronic Kidney Disease in Diabetes

BACKGROUND

Chronic renal inflammation and fibrosis are common sequelae in diabetes mellitus (DM) and are major causes of premature mortality. Although upregulation of NPPC expression occurs in response to renal inflammation in experimental animals, nothing is known of the molecular forms of C-type natriuretic peptide (CNP) products in urine of people with DM or links with renal function.

METHODS

ProCNP products in urine were characterized with HPLC and a range of antisera directed to specific epitopes of amino-terminal proCNP (NTproCNP). The 5-kDa intact peptide was quantified in spot urine samples from healthy adults and 202 participants with DM selected to provide a broad range of renal function.

RESULTS

The predominant products of proCNP in urine were consistent with the 2-kDa fragment (proCNP 3–20) and a smaller peak of intact (5-kDa) fragment (proCNP 1–50, NTproCNP). No peaks consistent with bioactive forms (proCNP 82–103, 50–103) were identified. The urine NTproCNP to creatinine ratio (NCR) was more reproducible than the albumin to creatinine ratio (ACR) and strongly associated with the presence of chronic kidney disease. In models predicting independence, among 10 variables associated with renal function in DM, including plasma NTproCNP, only 3 (sex, ACR, and plasma creatinine) contributed to NCR.

CONCLUSIONS

Characterization of the products of proCNP in urine confirmed the presence of NTproCNP. In spot random urine from study participants with DM, NCR is inversely associated with estimated glomerular filtration rate. In contrast to ACR, NCR reflects nonvascular factors that likely include renal inflammation and fibrosis.