# Untargeted LC–HRMS of Dried Blood Spots Reveals Metabolic Alterations and Candidate Biomarkers in Glutaric Aciduria Type-1

## TL;DR

This study uses untargeted metabolomics to identify metabolic changes and potential biomarkers in patients with Glutaric Aciduria Type-1.

## Contribution

The study introduces a novel use of untargeted LC–HRMS to discover candidate biomarkers for GA-1.

## Key findings

- 220 endogenous human metabolites were identified, with significant enrichment in carboxylic acids and derivatives.
- Metabolic alterations were observed in cardiolipin and phosphatidylcholine biosynthesis, pyrimidine metabolism, and the urea cycle.
- 6-Methylnonanoyl-CoA showed strong discriminative power as a potential biomarker for GA-1.

## Abstract

Background: Glutaric aciduria type-1 (GA-1) is a genetic disorder caused by glutaryl-coenzyme A dehydrogenase deficiency, leading to the accumulation of glutaryl-CoA and its derivatives. Clinical manifestations include neurological abnormalities; however, the underlying pathological mechanisms remain unclear. Early diagnosis and intervention are crucial for minimizing adverse outcomes. To date, diagnostic methods have certain limitations, and there is a critical need for a sensitive biomarker for diagnosis. We aimed to characterize metabolic dysregulation and identify candidate biomarkers associated with GA-1 in biochemically confirmed patients compared to age- and sex-matched control subjects. Methodology: Untargeted metabolomics profiling of GA-1 patients (n = 29) was compared to matched control subjects by age and sex. Multivariate and univariate statistical analyses were performed to identify dysregulated metabolites. Results: Our findings revealed 220 endogenous human metabolites. Notably, there was a strong enrichment in carboxylic acids and derivatives, including amino acids and derivatives, hydroxy and keto acids, fatty acyls, sphingolipids, phosphatidylcholines, and nucleotides and nucleosides. Pathway analysis indicates alterations in the biosynthesis of cardiolipin and phosphatidylcholine, as well as in pyrimidine metabolism, the urea cycle, and amino sugar metabolism. We demonstrated a robust performance model for 6-Methylnonanoyl-CoA, displaying strong discriminative power. Conclusions: We identified broad dysregulation across various biochemical classes, reflecting an imbalance in energy metabolism that involves carbohydrate and lipid pathways. The results also highlight dysregulation in sphingolipids, phospholipids, and nucleotide metabolism. These findings are preliminary and the clinical relevance of these findings in patients with GA-1 requires further investigation. We identified candidate biomarkers capable of distinguishing GA-1 patients from controls; however, these findings require validation in independent cohorts.