POS0753 METABOLOMICS AND LIPIDOMICS IN JUVENILE LOCALIZED SCLERODERMA (2024)

Background: Juvenile localised scleroderma (jLS) is a rare rheumatic disease in children characterized by inflammation and fibrosis in the skin [1, 2]. The cause and pathogenesis of jLS remain unclear, and both skin lesions and possible extracutaneous involvement may result in functional impairment and growth disturbances [2]. The treatment options to cure jLS are limited [3].

In recent years, omics technologies have been used to identify novel biomarkers in different diseases [4, 5]. Among the different omics technologies, metabolomics and lipidomics provide snapshots of the metabolic network.

Methods: Children with jLS and age-matched controls were recruited at Bambino Gesù Children’s Hospital, Roma, Italy. The characteristics of the participants are shown in Table 1.

Plasma samples from 9 controls and 12 patients with jLS (before treatment initiation and 17 months after treatment) were sent to Swedish Metabolomics Center, where liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry were performed (Figure 1A). Peak intensities were recorded, and the data analysis was performed using Metaboanalyst 5.0 and Graphpad Prism 10 software. Pathway enrichment bubble plots were generated using SRplot [6]. Mann-Whitney test was used to compare healthy control and baseline patient groups, and Wilcoxon test was used to compare differences between baseline and treated patients.

REFERENCES: [1] Zulian, F., et al., Consensus-based recommendations for the management of juvenile localised scleroderma. Ann Rheum Dis, 2019. 78(8): p. 1019-1024.

[2] Li, S.C. and R.J. Zheng, Overview of Juvenile localized scleroderma and its management. World J Pediatr, 2020. 16(1): p. 5-18.

[3] Li, S.C., Treatment of juvenile localized scleroderma: current recommendations, response factors, and potential alternative treatments. Current Opinion in Rheumatology, 2022. 34(5): p. 245-254.

[4] Puentes-Osorio, Y., et al., Potential clinical biomarkers in rheumatoid arthritis with an omic approach. Autoimmunity Highlights, 2021. 12(1).

[5] Xiao, Y.A., et al., Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis. Ebiomedicine, 2022. 79.

[6] Tang, D., et al., SRplot: A free online platform for data visualization and graphing. PLoS One, 2023. 18(11): p. e0294236.