Abstract
Community Acquired – Acute Kidney Injury (CA-AKI) is a sudden structural damage as well as loss of kidney function in otherwise healthy individual outside of hospital settings having high morbidity and mortality rates worldwide. Our objective was to identify urine proteins profile in CA-AKI subjects that can differentiate recovered and incompletely recovered CA-AKI patients at the time of hospital discharge and can be used as potential biomarkers to predict recovery from CA-AKI. We screened and enrolled 38 subjects aged 18-70 years and diagnosed to have CA-AKI as per KDIGO criteria and included 20 subjects in analysis. Urine and blood samples were collected at the time of hospital discharge, patients were followed up at 1 month and 4 month after discharge. Based on the outcome at 4 month, subjects were divided into group 1 (recovered incompletely) and group 2 (recovered completely). 10 subjects were randomly selected from each group and the urine samples were processed and subjected to mass spectrometric analysis by LC-MS/MS. Baseline characteristics of the 20 subjects at the time of discharge showed no significant differences in group 1 and 2 except SCr (2.19 ± 1.65 mg/dL and 4.16 ± 1.34 mg/dL, P=0.009). A total of 1983 proteins were detected from LC-MS/MS analysis. We short-listed 9 proteins in each group based on abundance ratio of > 2.0 and < 0.4 and p<0.05 (incompletely recovered / completely recovered) followed by their tissue specificity and involvement in pathway occurring in excretory organ or in kidney related disease(s) such as oxidative stress response, vitamin D metabolism and pathway. Further, we determined protein-protein interaction and pathway relatedness between shortlisted proteins and found that only actin-related protein 2/3 complex subunit 5 and myosin-11 from group 1 had physical interaction. We found variation in level of some proteins at the time of discharge in patients that recover either completely or incompletely at 4 months after discharge. Validation of these proteins in larger population set can provide us biomarker for progress of AKI to Chronic kidney disease.
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