SAT0487 PREDICTIVE BIOMARKERS OF I IgA VASCULITIS WITH NEPHRITIS BY METABOLOMIC ANALYSIS

Abstract

Background:IgA vasculitis/ Henoch Schönlein Purpura (IgAV/HSP) is the most common vasculitis of childhood and renal involvement is the most serious long-term complication. A better understanding of the pathophysiology of the progression to kidney disease is required for better treatment to be achieved and current biomarkers of Ig A vasculitis with nephritis (IgAVN) lack the predictive value.Objectives:In this study, an untargeted metabolomics approach was performed to reveal the underlying molecular mechanism of disease pathogenesis and to find potential biomarkers of plasma samples from patients with IgAV and IgAVN.Methods:IgAV/HSP was diagnosed according to the Ankara criteria in 2008 (1). Forty-five patients, including 39 active IgAV patients (H), 6 IgAVN (N), and 6 age- and gender-matched healthy controls (C), were enrolled in the study. Plasma samples from subjects were collected on the same day of IgAV(HSP) diagnosis and before steroid or other immunosuppressive treatment initiated. This study has utilized liquid chromatography-mass spectrometry (LC-MS/ Q-TOF) to investigate the alterations in plasma metabolomic profiles. Three separate pools, health controls, active IgAV, and IgAVN were created. Peak picking, grouping, and comparison parts were performed (metabolite profiling) via XCMS (https://xcmsonline.scripps.edu/) software.Results:Totally 2618 peaks were detected for group H, N and C. Among them 355 peaks were found to be statistically significant and reliable (p<0.05) and 155 of these peaks were found to be changed (fold change >1.5) between the groups C and H. On the other hand, 66 peaks were found to be changed (fold change >1.5) between the groups H and N. The number of the peaks on the intersection of the peaks found to be changed between the groups (C and H) and (H and N) was 39. This situation was illustrated in Figure 1. Based on putative identification results, 15 peaks were matched with 24 metabolites. The list of these metabolites is given in Table 1.Table 1.Putative identification of 15 peaks found to be statistically different and having fold changes.PeakPutative IdentificationKEGG Codesrt (min)N/H15-AminopentanamideC009902,100,3615-Aminopentanoic acidC004312,100,3612-Keto-6-aminocaproateC032392,100,361(S)-5-Amino-3-oxohexanoateC036562,100,361D-1-Piperideine-2-carboxylic acidC040922,100,362OxalureateC0080215,520,503PorphobilinogenC0093114,789,624(+)-cis-3,4-Dihydrophenanthrene-3,4-diolC0446815,520,025(-)-trans-CarveolC0096412,691,826DHAP(18:0)C0380512,701,926N-Acetyl-b-glucosaminylamineC0123912,701,9275-Methyltetrahydrofolic acidC0044014,882,418N2-Succinyl-L-ornithineC0341515,520,058N6-Acetyl-LL-2,6-diaminoheptanedioateC0439015,520,059EstroneC0046812,711,9010N-Acetyl-4-O-acetylneuraminic acidC0401514,892,4110N-Acetyl-7-O-acetylneuraminic acidC0401614,892,4111Oleoyl-CoAC0051015,520,2612SaccharopineC0044915,520,2613Prostaglandin D2C0069617,812,0513Prostaglandin I2C0131217,812,0514Glycocholic acidC0192113,780,6115GalactosylsphingosineC0174724,410,4515GlucosylsphingosineC0310824,410,45Figure 1.The number of the peaks found on datamining processConclusion:Certain differences in metabolites were identified between controls and IgAV patients and between those with and without kidney involvement.