Metabolome panels as potential noninvasive biomarkers for Primary Glomerulonephritis Sub-types: Meta-analysis of Profiling Metabolomics Studies

Abstract

Background and Aims Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease (CKD) worldwide. Renal biopsy, as an invasive method, is the main PGDs diagnosis approach. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consistent panels of dysregulated metabolites in PGD sub-types. Methods The PGDs-related metabolome profiles from urine, blood, and tissue samples were searched. Amanida package in R software was utilized for performing the meta-analysis. Through different sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. Results After a vigorous search, among the 25 selected studies (29 metabolite profiles), 832 dysregulated metabolites were recognized in 1519 PGN and control samples. Through different subtype analyses by Amanida package, the consensus list of metabolites in each category was obtained. Due to the importance of urinary metabolites, top dysregulated metabolites (vote score of ≥4 or ≤-4) were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. Conclusion The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.