Abstract
Background and Aims
Contrast induced nephropathy (CIN) has been reported to be the third foremost cause of acute renal failure. Metabolomics is a robust technique that has been used to identify potential biomarkers for early detection of renal damage after procedures with using contrast media. We aim to analyze the serum and urine metabolites changes, after using contrast for coronary angiography, to determine if metabolomics can use as a tool for early detection of CIN.
Method
Sixty-six patients with positive primary non-invasive diagnostic tests for coronary artery disease (CAD) who were candidate of elective coronary angiography recruited. Spot urine samples collected in the morning before angiography and 4 hours after angiography. Patients with > 0.5 mg/dL creatinine rise compared to baseline were considered as case (CIN group). Urine samples were centrifuged at 3000 rcf for 20 minutes at 4°C to remove the cell debris and after addition of sodium azide to prevent bacterial growth, were stored at -80 degree Celsius in aliquots until required. The mixtures were then transferred to a standard 5 mm NMR tube for analysis. 1H-NMR spectra were acquired at 300 K on a Bruker DRX 500 MHz spectrometer by using Carr–Pucell–Meiboom–Gill (CPMG) technique. For each spectrum, 154 scans were collected into 32K data points using a spectral width of 8389.26 Hz during the relaxation time of 2.5 s.
Results
Structure and outliers of the dataset composed of patient with CIN (n = 10) before angiography and after angiography were evaluated by PCA. A model with two principal components (PC1 and PC2) with R2X = 0.775 and Q2(cum) = 0.487 was obtained .A supervised OPLS-DA model was built to identify discriminative variables between metabolite profiles before and after angiography in patients with CIN. The high level of AUC 0.95 that was obtained from 10-fold cross validation besides decreased R2 (0.0, 0.415) and Q2 (0.0, -0.454) intercepts of 999 random permutations reflects the good validity of this diagnostic model. According to this valid OPLS-DA model, 15 chemical shifts were significant based on VIP > 1 and FC > 1.2. To check these suggested chemical shifts if their changes are due to kidney injury and not caused by contrast agent, a decoy OPLS-DA model was built for non-CIN patients before and after angiography .Two common significant chemical shifts (2.42 and 2.78 ppm) were found in comparison of these two models (i.e. before vs. after angiography in CIN group in compared with before vs. after angiography in non-CIN group) and were excluded from the results. Metabolites corresponding with the remaining list of 12 significant chemical shifts were identified and suggested as early detection biomarker candidates for CIN (Fig 1). The AUC value of a panel of four biomarker candidates were higher than single biomarkers that reflects the value of simultaneous measurement of these four metabolite candidates than single candidates. Figure 2 shows the list of diagnostic metabolite candidates with p < 0.05 . Pathway characterization was used to better understanding of pathophysiology of CIN. As the input data was small list of metabolites, only “Histidine_ lysine_ phenylalanine, tyrosine, proline and tryptophan catabolism” pathway (p < 0.05) was significant and suggested as the most important disturbed pathway in CIN.
Conclusion
Early detection of CIN as early as only 4 hours after using contrast can help better management of these patients. In this study, only after 4 hours passed from using contrast a panel of metabolites could be found in urine of patients who develop CIN, which facilitates early detection of CIN. This is the first study to investigate urine metabolic profile using NMR-based metabolomics for early detection of CIN after coronary angiography. The use of this suggested panel might significantly improve clinical consequences of this harmful complication.
pH-Dependent Piecewise Linear Correlation of 1H,31P Chemical Shifts: Application in NMR Identification of Nerve Agent Metabolites in Urine Samples