A Paradigm to Discover Biomarkers Associated With Chronic Kidney Disease Progression

Accuracy of glomerular filtration rate estimation using creatinine and cystatin C for identifying and monitoring moderate chronic kidney disease: the eGFR-C study

Background

Estimation of glomerular filtration rate using equations based on creatinine is widely used to manage chronic kidney disease. In the UK, the Chronic Kidney Disease Epidemiology Collaboration creatinine equation is recommended. Other published equations using cystatin C, an alternative marker of kidney function, have not gained widespread clinical acceptance. Given higher cost of cystatin C, its clinical utility should be validated before widespread introduction into the NHS.

Objectives

Primary objectives were to: (1) compare accuracy of glomerular filtration rate equations at baseline and longitudinally in people with stage 3 chronic kidney disease, and test whether accuracy is affected by ethnicity, diabetes, albuminuria and other characteristics; (2) establish the reference change value for significant glomerular filtration rate changes; (3) model disease progression; and (4) explore comparative cost-effectiveness of kidney disease monitoring strategies.

Design

A longitudinal, prospective study was designed to: (1) assess accuracy of glomerular filtration rate equations at baseline (n = 1167) and their ability to detect change over 3 years (n = 875); (2) model disease progression predictors in 278 individuals who received additional measurements; (3) quantify glomerular filtration rate variability components (n = 20); and (4) develop a measurement model analysis to compare different monitoring strategy costs (n = 875).

Setting

Primary, secondary and tertiary care.

Participants

Adults (≥ 18 years) with stage 3 chronic kidney disease.

Interventions

Estimated glomerular filtration rate using the Chronic Kidney Disease Epidemiology Collaboration and Modification of Diet in Renal Disease equations.

Main outcome measures

Measured glomerular filtration rate was the reference against which estimating equations were compared with accuracy being expressed as P30 (percentage of values within 30% of reference) and progression (variously defined) studied as sensitivity/specificity. A regression model of disease progression was developed and differences for risk factors estimated. Biological variation components were measured and the reference change value calculated. Comparative costs of monitoring with different estimating equations modelled over 10 years were calculated.

Results

Accuracy (P30) of all equations was ≥ 89.5%: the combined creatinine-cystatin equation (94.9%) was superior (p < 0.001) to other equations. Within each equation, no differences in P30 were seen across categories of age, gender, diabetes, albuminuria, body mass index, kidney function level and ethnicity. All equations showed poor (< 63%) sensitivity for detecting patients showing kidney function decline crossing clinically significant thresholds (e.g. a 25% decline in function). Consequently, the additional cost of monitoring kidney function annually using a cystatin C-based equation could not be justified (incremental cost per patient over 10 years = £43.32). Modelling data showed association between higher albuminuria and faster decline in measured and creatinine-estimated glomerular filtration rate. Reference change values for measured glomerular filtration rate (%, positive/negative) were 21.5/-17.7, with lower reference change values for estimated glomerular filtration rate.

Limitations

Recruitment of people from South Asian and African-Caribbean backgrounds was below the study target.

Future work

Prospective studies of the value of cystatin C as a risk marker in chronic kidney disease should be undertaken.

Conclusions

Inclusion of cystatin C in glomerular filtration rate-estimating equations marginally improved accuracy but not detection of disease progression. Our data do not support cystatin C use for monitoring of glomerular filtration rate in stage 3 chronic kidney disease.

Trial registration

This trial is registered as ISRCTN42955626.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 11/103/01) and is published in full in Health Technology Assessment; Vol. 28, No. 35. See the NIHR Funding and Awards website for further award information.

Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort

BACKGROUND

The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction.

METHODS

Using the Salford Kidney Study biobank, a UK-based prospective cohort of over 3000 non-dialysis CKD patients, 433 patients met our inclusion criteria: a minimum of four eGFR measurements over a two-year period and a linear eGFR trajectory. Plasma samples were obtained and analysed for novel proteomic signals using SWATH-Mass-Spectrometry. The 4-variable UK-calibrated KFRE was calculated for each patient based on their baseline clinical characteristics. Boruta machine learning algorithm was used for the selection of proteins most contributing to differentiation between patient groups. Logistic regression was employed for estimation of ESRD prediction by (1) proteomic features; (2) KFRE; and (3) proteomic features alongside KFRE.

RESULTS

SWATH maps with 943 quantified proteins were generated and investigated in tandem with available clinical data to identify potential progression biomarkers. We identified a set of proteins (SPTA1, MYL6 and C6) that, when used alongside the 4-variable UK-KFRE, improved the prediction of 5-year risk of ESRD (AUC = 0.75 vs AUC = 0.70). Functional enrichment analysis revealed Rho GTPases and regulation of the actin cytoskeleton pathways to be statistically significant, inferring their role in kidney function and the pathogenesis of renal disease.

CONCLUSIONS

Proteins SPTA1, MYL6 and C6, when used alongside the 4-variable UK-KFRE achieve an improved performance when predicting a 5-year risk of ESRD. Specific pathways implicated in the pathogenesis of podocyte dysfunction were also identified, which could serve as potential therapeutic targets. The findings of our study carry implications for comprehending the involvement of the Rho family GTPases in the pathophysiology of kidney disease, advancing our understanding of the proteomic factors influencing susceptibility to renal damage.

Proteogenomics in Nephrology: A New Frontier in Nephrological Research

Proteogenomics represents a transformative intersection in nephrology, uniting genomics, transcriptomics, and proteomics to unravel the molecular intricacies of kidney diseases. This review encapsulates the methodological essence of proteogenomics and its profound implications in chronic kidney disease (CKD) research. We explore the proteogenomic pipeline, highlighting the integrated analysis of genomic, transcriptomic, and proteomic data and its pivotal role in enhancing our understanding of kidney pathologies. Through case studies, we showcase the application of proteogenomics in clear cell renal cell carcinoma (ccRCC) and Autosomal Recessive Polycystic Kidney Disease (ARPKD), emphasizing its potential in personalized treatment strategies and biomarker discovery. The review also addresses the challenges in proteogenomic analysis, including data integration complexities and bioinformatics limitations, and proposes solutions for advancing the field. Ultimately, this review underscores the prospective future of proteogenomics in nephrology, particularly in advancing personalized medicine and providing novel therapeutic insights.

Current and Novel Biomarkers of Progression Risk in Children with Chronic Kidney Disease

BACKGROUND

Due to the complexity of chronic kidney disease (CKD) pathophysiology, biomarkers representing different mechanistic pathways have been targeted for the study and development of novel biomarkers. The discovery of clinically useful CKD biomarkers would allow for the identification of those children at the highest risk of kidney function decline for timely interventions and enrollment in clinical trials.

SUMMARY

Glomerular filtration rate and proteinuria are traditional biomarkers to classify and prognosticate CKD progression in clinical practice but have several limitations. Over the recent decades, novel biomarkers have been identified from blood or urine with metabolomic screening studies, proteomic screening studies, and an improved knowledge of CKD pathophysiology. This review highlights promising biomarkers associated with the progression of CKD that could potentially serve as future prognostic markers in children with CKD.

KEY MESSAGES

Further studies are needed in children with CKD to validate putative biomarkers, particularly candidate proteins and metabolites, for improving clinical management.