The determination of early potential biomarkers for Diabetic Nephropathy in human urine samples using ultra high-performance liquid chromatography coupled to quadrupole time of flight high resolution mass spectrometer (UHPLC - QTOF HRMS)

Abstract

Diabetes is a chronic disease that causes serious global challenges to the health care system and continues to increase in the adult population. The lack of insulin or insulin action is the primary cause of diabetes. Diabetes has several complications including retinopathy, neuropathy, and nephropathy which affect the eyes, nerves, and kidneys respectively. Diabetic nephropathy remains the main cause of chronic kidney disease and the current diagnostic methods lack specificity, selectivity and cannot detect the disease early and/or accurately monitor the progression of the disease.

The aim of this study was to develop a quantitative ultra-high performance liquid chromatography coupled to high resolution mass spectrometry method for measuring amino acids concentration ratios in urine samples of non-diabetic and diabetic patients which can be used by South African hospitals as an early diagnostic tool of diabetic nephropathy.

A liquid chromatography mass spectrometry method with optimum separation conditions of target amino acids (arginine, citrulline, isoleucine, ornithine, phenylalanine, and tyrosine) on a C18 column was developed. The developed method was validated for linearity R2 in the analytical range of 0.01 mg/L to 5.00 mg/L. The accuracy (%recovery) and precision (%RSD), of the developed method was determined by analysing quality control samples spiked with amino acid concentrations of 0.10, 0.30, 0.50, 3.00, and 4.00 mg/L in between runs and on two different days. The validated method was then used to analyse urine samples qualitatively and quantitatively.

The collection of spot urine samples from two groups of participants: the diabetes (glycated haemoglobin > 6.4%) group and non-diabetic patients (glycated haemoglobin ≤ 6.4%) group was conducted at Chris Hani Baragwanath Hospital, South Africa. The degree of renal function predicted by the estimated glomerular filtration (eGFRs) was used to categorise samples into 5 stages of chronic kidney disease. The qualitative mass spectrometry data obtained from the analysis of urine samples was submitted to the MetaboAnalyst data base to determine pathways affected by diabetic nephropathy at different stages of chronic kidney disease and to also identify compounds that can be used as biomarkers for both the onset and progression of the disease.

The linearity R2 on day one ranged from 0.960-0.999, while on day two it was 0.962- 0.999 therefore the method showed good accuracy for all the amino acids over the two days analysis period. Amino acid concentration ratios of ornithine/arginine, tyrosine/phenylalanine, and citrulline/ornithine showed statistically significant differences between the control and the chronic kidney disease patient groups (stage 1 and stage 3). These amino acids ratios may have the potential to indicate both the onset and progression of the disease.

Several biomarkers were identified in the qualitative analysis, but L-arginine was the most sensitive biomarker for early chronic kidney disease as well as for disease progression. The metabolism of D-glutamine was one of the pathways that was strongly impacted by the disease and could be a predictor for the disease progression.

The study demonstrated that both the quantitative data and qualitative data obtained from the mass spectrometry, when used together or independently can assist in the biomarker discovery for diabetic nephropathy. It also showed that mass spectrometry is a promising technique for the detection and quantification of biomarkers, and the method would be revalidated to ensure that all the validation criteria are met