Coronary Angiography Contrast Research Articles

SO015URINE NMR-BASED METABOLOMICS FOR EARLY DETECTION OF POST-PROCEDURAL CONTRAST NEPHROPATHY

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.

Contrast Nephropathy Review

Contrast-induced nephropathy (CIN) is a common cause of acute kidney dysfunction. It is necessary to identify at-risk patients at early stages to implement preventive strategies to decrease the incidence of this nephropathy. However, mechanisms of CIN have not fully explained yet. Most predictive models for contrast-induced nephropathy in clinical use have modest ability and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient-centred decision making, as well as improve the use of prevention strategies for contrast-induced nephropathy.

Urine and serum NMR-based metabolomics in pre-procedural prediction of contrast-induced nephropathy.

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 the prediction of renal damage. We aim to analyze the serum and urine metabolites changes, before and after using contrast for coronary angiography, to determine if metabolomics can predict early development of CIN. 66 patients undergoing elective coronary angiography were eligible for enrollment. Urine and serum samples were collected prior to administration of CM and 72h post procedure and analyzed by nuclear magnetic resonance. The significant differential metabolites between patients who develop CIN and patients who have stable renal function after angiography were identified using U test and receiver operating characteristic analysis was performed for each metabolite candidate. Potential susceptible pathways to cytotoxic effect of CM were investigated by pathway analysis. A predictive panel composed of six urinary metabolites had the best area under the curve. Glutamic acid, uridine diphosphate, glutamine and tyrosine were the most important serum predictive biomarkers. Several pathways related to amino acid and nicotinamide metabolism were suggested as impaired pathways in CIN prone patients. Changes exist in urine and serum metabolomics patterns in patients who do and do not develop CIN after coronary angiography hence metabolites may be potential predictive identifiers of CIN.