Exploring disease-specific metabolite signatures in hereditary angioedema patients

Abstract

Introduction: Hereditary angioedema (HAE) is a rare, life-threatening autosomal dominant genetic disorder caused by a deficient and/or dysfunctional C1 esterase inhibitor (C1-INH) (type 1 and type 2) leading to recurrent episodes of edema. This study aims to explore HAE patients' metabolomic profiles and identify novel potential diagnostic biomarkers for HAE. The study also examined distinguishing HAE from idiopathic angioedema (AE).

Methods: Blood plasma samples from 10 HAE (types 1/2) patients, 15 patients with idiopathic AE, and 20 healthy controls were collected in Latvia and analyzed using LC-MS based targeted metabolomics workflow. T-test and fold change calculation were used to identify metabolites with significant differences between diseases and control groups. ROC analysis was performed to evaluate metabolite based classification model.

Results: A total of 33 metabolites were detected and quantified. The results showed that isovalerylcarnitine, cystine, and hydroxyproline were the most significantly altered metabolites between the disease and control groups. Aspartic acid was identified as a significant metabolite that could differentiate between HAE and idiopathic AE. The mathematical combination of metabolites (hydroxyproline * cystine)/(creatinine * isovalerylcarnitine) was identified as the diagnosis signature for HAE. Furthermore, glycine/asparagine ratio could differentiate between HAE and idiopathic AE.

Conclusion: Our study identified isovalerylcarnitine, cystine, and hydroxyproline as potential biomarkers for HAE diagnosis. Identifying new biomarkers may offer enhanced prospects for accurate, timely, and economical diagnosis of HAE, as well as tailored treatment selection for optimal patient care.

Keywords: C1 inhibitor (C1-INH); biomarkers; diagnosis; hereditary angioedema; metabolome.

Figure 1

Correlation heatmap.

Figure 2

Significantly changed metabolites. (A) Volcano plots depicting fold change (FC) and p-value of metabolite concentration in different sample groups, thresholds: log2(fold change)>2 or <-2, and -log10(p)>1. (B) Bar plots for most significantly changed metabolites (*p-value < 0.05, **p-value <0.005, ***p-value <0.0005).

Figure 3

ROC analysis for biomarkers. (A) ROC curve of biomarker signature to distinguish between HAE and healthy controls (CNT). The cutoff value with highest sensitivity and selectivity indicated. (B) ROC curve of biomarker signature to distinguish between AE and HAE. The cutoff value with highest sensitivity and selectivity indicated.