INVESTIGATION OF URINARY METABOLOMIC PROFILING FOLLOWING EXPOSURE TO ULTRAVIOLET RADIATION-A BY USING LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY (LC-MS)

ALI MUHSEN ALI; AHMED M. ZHEOAT; HUSSEIN ALI KAREEM; MUSTAFA KAREEM HASSAN

doi:10.22159/ijap.2025v17i4.54103

Authors

ALI MUHSEN ALI Pathological Analysis Department, Faculty of Applied Medical Science, Al-Shatrah University, Iraq
AHMED M. ZHEOAT Al-Manara College for Medical Sciences, Iraq
HUSSEIN ALI KAREEM Pathological Analysis Department, Faculty of Applied Medical Science, Al-Shatrah University, Iraq
MUSTAFA KAREEM HASSAN Pathological Analysis Department, Faculty of Applied Medical Science, Al-Shatrah University, Iraq https://orcid.org/0000-0001-7275-4942

DOI:

https://doi.org/10.22159/ijap.2025v17i4.54103

Keywords:

LC–MS, Ultraviolet radiation (UV), Urine Metabolomic profiling, Principal Components Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

Abstract

Objective: This study is designed to explore the impact of Ultraviolet-A (UVA) radiation on the urinary metabolome. The key novelty of this research lies in its focus on recording and analysing UVA-induced modifications, a less explored aspect in human metabolomic studies. Thus, study’s aim is identification specific metabolomic changes associated with UVA exposure, emphasizing its potential implications in health and disease biomarker discovery.

Methods: Twenty-Eight urine samples have been collected from fourteen adult healthy individual sat pre-UV exposure (UVS1) and post-UV exposure (UVS2) conditions. Urinary metabolomic profiling was detected by using Liquid Chromatography – High Resolution Mass Spectrometry (LC-HRMS) Data modelling by Principal Components Analysis (PCA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) were applied after data extraction and normalizing.

Results: Multivariate data analysis by PCA and OPLS-DA models confirm clear separation of the observations as a marked difference based on R2Xcum 0.795, Q2cum 0.590 and R2Xcum: 0.69, R2Ycum: 0.98 and Q2cum: 0.83, respectively, in metabolite profile among study groups, non-exposure and UV-exposure. According for these effects, our results identified significant metabolic changes for important seventeen urinary metabolites. The pathway of significantly metabolites were marked in amino acids (Histidine, Tyrosine, Lysine, Arginine and proline metabolism), carbohydrate and lipids and their derivatives.

Conclusion: Current study concluded that these metabolites may be a potential biomarkers for predictive the impacts of UV sourced by sunlight exposure on some metabolic pathways, including: amino acid, carbohydrates, lipids, peptides, xenobiotics, and Co-factors and vitamins metabolism, which may be influenced by UV sunlight exposure.

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