LAUSR

BACKGROUND IgA vasculitis (IgAV) is the most common vasculitis of childhood. Renal involvement defines late morbidity of the disease. A better understanding of the pathophysiology of the progression to kidney disease and predictive biomarkers are required for better management of IgAV and its nephritis (IgAVN). OBJECTIVES An untargeted metabolomics approach was performed to reveal the underlying molecular mechanism of disease pathogenesis and to define potential biomarkers from plasma samples from IgAV and IgAVN patients. METHODS Forty-five active IgAV patients (H) and six healthy controls (C) were enrolled in the study. Plasma samples were collected on the same day of diagnosis and before any immunosuppressive treatment was initiated. At the time of diagnosis and sample collection, none of the patients had renal involvement. We used Quadrupole Time of Flight Mass Spectrometry (Q-TOF LC/MS) to investigate the alterations in plasma metabolomic profiles. Three separate pools were created: healthy controls (group C), active IgAV patients who did not develop renal involvement (group H), and patients who developed IgAVN at follow up (group N). Peak picking, grouping, and comparison parts were performed via XCMS (https://xcmsonline.scripps.edu/) software. RESULTS At follow-up, IgAVN developed in 6 out of 45 IgAV patients. The median time of renal involvement development is 23 days (range 5-45 days). Of these, 3 had nephritic proteinuria, one had nephrotic proteinuria, and 2 had microscopic hematuria. There were no significant differences in gender, age, clinical manifestations, and laboratory findings between the six patients who developed renal involvement and those who did not. In multivariate analysis, there was no significant association between any of the defined demographic and clinical characteristics (male sex, gastrointestinal system involvement, joint involvement, CRP, WBC, PLT) and the occurrence of renal involvement. 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, and 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 different between the groups (C and H) and (H and N) was 39. Based on putative identification results, 15 putatively identified metabolites matched with 11 peaks were presented as biomarker candidates after careful evaluation with a clinical perspective. CONCLUSION We suggest that DHAP (18:0), prostaglandin D2/I2, porphobilinogen, 5-methyltetrahydrofolic acid, and N-Acetyl-4-O-acetylneuraminic acid/N-Acetyl-7-O-acetylneuraminic acid may serve as biomarkers for predicting kidney disease. Future studies with larger groups of IgAV patients are needed to validate the identified metabolic profile.