UTILITY OF PROTEIN BIOMARKERS IN URINARY

Currently, monitoring the levels of albuminuria/proteinuria remains the only method of diabetic kidney disease (DKD) risk stratification in clinical practice. Urinary exosomes (UEs) have been evaluated as a potential non-invasive biomarker in DKD but the process of UE isolation remains laborious. Whole urine shares a fraction of detectable proteins from UEs and here we aim to explore the utility of using both mediums to establish potential biomarkers for risk stratification.

Thirty-one patients were recruited from Nephrology Clinics at National University Hospital in Singapore between 2021 to 2022. Inclusion criteria includes: (i) 21 to 80 years of age; (ii) G2-4 CKD (eGFR calculated using CKD-EPI equation) and (iii) history of type 2 diabetes mellitus with proven diabetic retinopathy (DR), or without DR but with either A2-A3 albuminuria (KDIGO guidelines), proteinuria >0.5g/day, or biopsy proven DKD. We collected baseline demographics, plasma and urine samples. UEs were isolated using ultracentrifugation and UE markers (ALIX and TSG101) were confirmed via western blot assays. Mass spectrometry (MS)-based experiments were used to profile the proteins across samples and characterized the proteins detectable in the two sample mediums. We stratified the patients into four risk categories (Moderate, Moderate-to-high, high and very high risk) to identify potential urine markers.

We identified 2698 proteins in UEs compared to 1363 proteins in whole urine, where 974 proteins overlapped. The 389 proteins uniquely identified in whole urine consists of cell surface and receptor proteins involved in mesenchyme development and extracellular matrix organization. The 1724 proteins uniquely identified in exosomes were part of proteasome and endopeptidase complexes, involved in protein degradation and protein transport. The correlation of proteins across patients revealed that most of them were not well presented in the two mediums. Only 25 proteins had a correlation above 0.7, including alpha 1-microglobulin/bikunin precursor (AMBP, r=0.883), followed by peptidoglycan recognition protein (PGLYRP1, r=0.870) and regucalcin (RGN, r=0.840). Analysis of patient-specific correlation of proteins in the two mediums showed 12 discordant proteins that were outside of 3SD in at least 5 patients (20%). Proteasome 20S subunit beta 9 (PSMB9), a renal tubulopathy marker, was most different and outside of 3SD across 10 patients (40%), Lastly, 288 proteins were significantly different across the risk categories using whole urine compared to 113 using UEs, where 48 proteins overlapped. Four potential urine biomarkers, cystatin-C (CST3), alpha-enolase (ENO1), complement factor B (CFB) and uromodulin (UMOD), were shortlisted illustrating consistent trends across the risk groups in both whole urine and UEs.

Table 1. Table showing the characteristics across 31 patients recruited in the study.

UEs carry important biological information but are difficult and laborious to extract, making it impractical for clinical practice. Whole urine on the other hand is easy to profile but contains a lot of waste products not useful for understanding functions involved in the progression of kidney diseases. Using MS-based proteomics, we identified potential protein biomarkers, representative of both mediums, useful for risk stratification for DKD progression.